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Reports to: Co-Founder & Director

Role Summary

As Head of Engineering and Product Architect at ConForge AI, you own the technical direction, product architecture, engineering execution, and team-building for everything we build. You are the technical center of gravity of the company — accountable for what we ship, how we ship it, who ships it, and how well it holds up over time. You partner directly with the CEO on strategic direction, act as the primary technical face to our anchor client, and build the engineering organization from a small founding team into a durable one. This role is architect and builder in equal measure: you set the direction, and you write meaningful parts of the thing you designed.

Key Responsibilities

Product & Technical Architecture

• Architect ConForge AI's product portfolio end-to-end — the multi-agentic Agentic Company platform, AI systems, SaaS products, web applications, and mobile applications.
• Own core architectural decisions: technology stack, system design, data architecture, model architecture, deployment topology, security posture, and integration patterns.
• Design solutions across ConForge's services engagements (marketing, sales/BD/proposal, estimating, engineering, accounting, HR, IT/CT) so every engagement produces reusable, platform-aligned capability rather than one-off work.

Hands-On Engineering

• Personally build and ship critical components of the product and platform — you architect it, you write meaningful parts of it, and you own the outcome end to end.
• Prototype new ideas, model integrations, and technical spikes yourself before handing them to the team, establishing feasibility and technical direction through your own code.
• Contribute to code reviews at a technical depth that raises the bar for the whole team; your own commits set the standard for quality, taste, and craft.
• Maintain expert-level familiarity with the codebase and infrastructure — you can debug production issues, pair on hard problems, and unblock engineers through direct technical work.

Engineering Leadership & Delivery

• Own the engineering team's execution: sprint planning, backlog prioritization, delivery cadence, and quality gates.
• Drive fast, high-quality delivery — balancing speed against the scalability, security, and maintainability the product needs long-term, and making that trade-off explicit rather than implicit.
• Define and enforce engineering standards: code review protocols, testing standards, deployment practices, incident response, on-call rotations, and security hygiene.
• Establish and evolve the SOPs that govern how the entire engineering and technical team works.

Team Building & People Management

• Build and grow the engineering team — hiring, onboarding, performance management, career growth, and retention across the SDE, data science, and construction technology tracks.
• Create a culture of ownership, high standards, and continuous learning, where engineers are trusted with real scope early.
• Coach and develop individual team members: set clear expectations, give regular feedback, and build career paths people can actually see themselves on.
• Delegate design authority as the team scales — grow Tech Leads and SDE-2s into escalation points rather than remaining the single technical bottleneck.

Internal Systems & Operations

• Set up and manage the internal technology that supports ConForge itself: development infrastructure, CI/CD, project management, communications, security, and data platforms.
• Deploy AI and automation across ConForge's own operations — development, sales, marketing, admin, and finance. We practice what we sell.
• Keep internal tooling modern and efficient so the team moves fast without friction.

AI-Native Practice & Technology Research

• Set the standard for AI-native engineering across the team — fluent, responsible use of AI coding assistants and agent tooling, with full ownership and understanding of every line shipped.
• Research and evaluate emerging frameworks, models, and protocols (A2A, AGUI, MCP) relevant to the product roadmap, and integrate what earns its place.
• Make deliberate build-vs-buy-vs-integrate decisions — adopting state-of-the-art capability where it accelerates the product, and shielding the team from hype cycles that don't.
• Own the product roadmap in partnership with the CEO, quarterly and annual, with clear owners, timelines, and success criteria.

Stakeholder Alignment & Collaboration

• Serve as the primary technical point of contact for the anchor client, translating business needs into product capability and product capability back into business value.
• Partner closely with the CEO on strategic decisions, prioritization trade-offs, and the technical narrative in fundraising and partner conversations.
• Align cross-functionally with sales, services delivery, and operations so the product supports what the business needs to sell and deliver.
• Represent ConForge externally on technical topics — client presentations, partner discussions, and hiring conversations.

What Success Looks Like

• The team ships. Sprints close on time, quality is high, incidents are rare, and each release moves the product visibly forward.
• The team grows. People join, stay, do their best work, and get better every quarter — and some of them grow into leadership under you.
• The product compounds. Every services engagement makes the platform stronger; every platform capability makes services faster.
• The anchor client wins. They see demonstrable value from ConForge's product inside their own operations, not in a demo environment.
• The platform is ready for the second client before the second client asks — multi-tenancy, isolation, and deployment automation are validated, not aspirational.
• The technology stays current. ConForge does not fall behind on the frameworks, models, or protocols that matter.

Required Experience and Skills

• Proven track record building and shipping enterprise-grade AI, SaaS, or platform products end-to-end — not just architecting them.
• Deep hands-on experience with modern AI/ML stacks: agent frameworks and orchestration, vector databases, RAG architectures, and LLM integration in production.
• Strong architectural foundations across web, mobile, cloud, data, and security, with the judgment to know which decisions are reversible and which are not.
• Demonstrated experience building and managing engineering teams; comfortable owning hiring, performance, and culture from zero.
• Track record of shipping under real business constraints — anchor customers, timelines, and budget — where the deadline was not negotiable.
• Fluency with AI-native engineering practice and the ability to set that standard for a team, retaining full ownership of what gets shipped.
• Strong communication skills, with the ability to translate technical concepts for business stakeholders and business needs into technical direction.

Preferred

• Experience as a founding or first engineering leader at an early-stage company.
• Exposure to construction, engineering, or another document- and process-heavy enterprise domain.
• Experience productizing services work into reusable, multi-tenant frameworks.

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Role Summary

As a DS-1 at ConForge AI, you execute well-defined data science tasks under the direction of a DS-2 or the HOE while building fluency in the team's pipelines, tooling, and evaluation practices. Mirroring the SDE-1 structure, your focus is guided, careful execution — dataset preparation, running experiments, and supporting evaluation — while developing the applied ML depth to grow into greater ownership on the data science ladder.

Key Responsibilities

Execution & Delivery

• Execute well-scoped data science tasks — dataset preparation, annotation/curation support, and running training and evaluation jobs — under the direction of a DS-2 or the HOE.
• Build and maintain data preparation and preprocessing scripts, including basic data-quality checks.
• Run experiments as specified, log results carefully, and report findings clearly.

Evaluation & Quality

• Help maintain evaluation datasets and run evaluation jobs; flag anomalies in metrics or in the underlying data.
• Keep experiments reproducible — versioned datasets, recorded parameters, and clear notes.

Technical Growth

• Build fluency in the team's ML/AI stack — data pipelines, vector/retrieval stores, and training/fine-tuning tooling.
• Develop applied ML depth (evaluation design, fine-tuning basics, and computer-vision pipeline fundamentals) toward DS-2 readiness.

AI-Native Practice

• Use AI coding assistants day-to-day for pipeline/tooling code and debugging — but review and understand every line before submitting it.
• Treat AI output as a draft to validate, not a final answer.

What Success Looks Like

• Assigned data and experiment tasks are completed correctly and on time, with minimal rework.
• Experiments are reproducible and clearly documented.
• Data-quality issues are caught and flagged early.
• You show a growing ability to scope a small data science task from a short brief.

Required Experience and Skills

• Foundational applied ML/statistics knowledge and comfort with Python and common data/ML libraries.
• Familiarity with — or eagerness to learn — model training/evaluation workflows and computer-vision pipeline basics.
• Comfortable with Git-based workflows and reproducible experiment practices.
• Eagerness to use AI coding assistants responsibly and to learn the team’s tooling.
• Clear communication with leads and peers; comfortable asking questions and raising blockers early.

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Role Summary

As a DS-2 at ConForge AI, you own the ML/AI lifecycle end-to-end for the team's AI features — from data curation through fine-tuning to production evaluation — with enough engineering fluency to ship models into a live system, not just prototype them. This role carries a particular focus on computer vision (CV): a meaningful share of the team's AI features involve visual and document data, so CV pipeline experience is central to the work.

Key Responsibilities

Data & Model Pipeline Ownership

• Own the data pipeline for model improvement: gold-pair curation, active-learning loop design, and data-quality gates (including cross-customer data isolation).
• Design and run fine-tuning pipelines (e.g., parameter-efficient fine-tuning approaches) for domain-specific model improvements.
• Prototype new AI capabilities (e.g., agent behaviors, retrieval strategies, state-tracking approaches) and hand off production-ready designs to engineering.

Evaluation & Quality

• Define and track evaluation metrics for AI features (e.g., acceptance-rate × time-saved composite metrics) and report on model quality over time.
• Design and validate the rollback/rollout strategy for model updates (shadow → canary → hot-swap) in partnership with engineering.
• Apply the same rigor to evaluating the team’s own AI features that a skeptical outside reviewer would.

Cross-Functional Partnership

• Partner with the HOE and SDE-2s on retrieval, knowledge-layer, and agent-orchestration design decisions that affect model performance.
• Communicate model quality, risk, and trade-offs clearly to engineering and non-technical stakeholders.

AI-Native Practice

• Use AI coding assistants to speed up pipeline/tooling code so more time goes to data quality and eval design.
• Stay current on fine-tuning and agent-architecture techniques broadly — not just the team's current approach — to keep the roadmap informed by the state of the art.

What Success Looks Like

• Model/data quality steadily improves quarter over quarter against defined eval metrics.
• Rollouts of model updates are safe and reversible — no unplanned regressions reach production.
• Your evaluation of the team’s own AI features holds up to outside scrutiny.
• Engineering trusts your data/eval judgment enough to build on it without re-litigating it.

Required Experience and Skills

• Strong applied ML background: fine-tuning methods, evaluation design, and classic ML/statistics fundamentals.
• Full-stack enough to productionize work — can write backend services, work with databases/vector stores directly, and integrate with agent orchestration frameworks.
• Understanding of production ML concerns: data drift, latency/cost trade-offs, and safe rollout patterns.
• Comfortable with experiment tracking and reproducibility (versioned datasets, versioned model artifacts).
• Hands-on experience building computer-vision (CV) pipelines — data collection/annotation, model training or fine-tuning, and evaluation for CV tasks — is required.

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Role Summary

As a Tech Lead at ConForge AI, you own technical design across the engineering team and act as a secondary reporting and escalation point for SDE-1s and SDE-2s, reducing the direct-report load on the HOE. You combine hands-on engineering with design leadership — setting technical direction, safeguarding quality, and growing the engineers around you as the team scales beyond a single-manager structure.

Key Responsibilities

Technical Design & Direction

• Own technical design for features and systems — architecture, interfaces, and cross-cutting patterns — in partnership with the HOE.
• Review and guide design approaches from SDE-1s and SDE-2s, ensuring consistency, scalability, and maintainability.
• Balance delivery speed against technical debt, making trade-off decisions explicit and documenting them.

Team Leadership & Escalation

• Act as a secondary reporting/escalation point for SDE-1s and SDE-2s on technical questions and blockers.
• Mentor engineers, grow their design judgment, and support the onboarding of new hires.
• Help plan and break down work, ensuring tickets are well-scoped and correctly prioritized.

Delivery & Quality

• Stay hands-on — contribute code on critical or high-risk paths and set the standard for quality.
• Own the code-review bar for the team and safeguard CI/CD health and release quality.
• Partner with data science on retrieval, knowledge-layer, and agent-orchestration design decisions.

AI-Native Engineering

• Drive effective, responsible adoption of AI coding assistants across the team, retaining full understanding and ownership of shipped code.
• Keep the team's engineering practices current with the evolving AI-native tooling landscape.

What Success Looks Like

• Technical designs hold up under implementation and scale with the product.
• SDE-1s and SDE-2s escalate to you effectively, measurably reducing load on the HOE.
• Engineers you mentor grow in autonomy and design judgment.
• Release quality and delivery predictability improve under your technical ownership.

Required Experience and Skills

• Strong full-stack engineering background with a track record of owning technical design for non-trivial systems.
• Demonstrated ability to mentor engineers and serve as a technical escalation point.
• Solid grasp of system-design patterns, scalability, and production quality / CI-CD practices.
• Fluency with modern AI coding assistants and agent-based tooling, used with full ownership of the output.
• Clear communicator able to align engineers and stakeholders on technical direction.

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Role Summary

As an SDE-1 at ConForge AI, you execute well-defined tasks with guidance from your lead or SDE-2, while building fluency in the codebase, tools, and engineering practices. Your primary focus is shipping small, correct changes — features, bug fixes, and enhancements — while developing the judgment and technical depth to grow into greater ownership.

Key Responsibilities

Execution & Delivery

• Implement well-scoped features, bug fixes, and small enhancements assigned by a lead or SDE-2.
• Write unit tests for your own code and ensure changes pass CI before requesting review.
• Act according to priority — work the highest-priority ticket assigned, and flag it if priorities seem unclear or conflicting.

Quality, Documentation & Communication

• Participate actively in code reviews — both giving and receiving feedback.
• Document code, configs, and setup steps as part of normal workflow, not an afterthought.
• Raise blockers early rather than sitting on them.

Technical Growth

• Build comfort in at least one part of the stack (frontend or backend), and grow toward full-stack capability.
• Learn to read and navigate the existing codebase, follow established patterns, and use Git effectively (branching, rebasing, PRs).
• Build familiarity with the team's data and AI infrastructure — databases, vector/retrieval stores, agent frameworks.

AI-Enabled Development

• Use AI coding assistants day-to-day for scaffolding, debugging, and learning unfamiliar parts of the codebase — but review and understand every line before submitting it.
• Be able to explain what an AI feature or agent step does when reading through the code, even without designing one from scratch yet.
• Treat AI output as a draft, not a final answer — check it rather than pasting it in blind.

What Success Looks Like

• You consistently ship correct, well-tested code with minimal rework after review.
• You can independently scope a small feature from a one-paragraph description.
• You proactively identify edge cases and ask the right clarifying questions upfront.
• Your tickets close on time, and your code reviews are substantive on both sides.

Required Experience and Skills

• Working proficiency in at least one part of the stack (frontend or backend).
• Comfortable with Git-based workflows and basic CI/CD concepts.
• Familiarity with — or eagerness to learn — modern AI coding assistants and agent-based tooling.
• Clear communication with leads and peers; comfortable asking questions and raising blockers early.
• Active interest in researching and staying current with software engineering best practices and standard system design patterns, and applying relevant learnings to your own work.

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Role Summary

As an SDE-2 at ConForge AI, you own features, services, and — as you grow in the role — whole systems end-to-end: design, implementation, testing, and rollout, with increasing autonomy over architectural decisions in your domain. You mentor SDE-1s and act as a technical reference point for your area.

Key Responsibilities

Ownership & Delivery

• Own a feature, service, or system end-to-end: design approach, implementation, tests, and deployment.
• Break down ambiguous requirements into an implementation plan without heavy hand-holding.
• Debug production issues in owned areas and drive them to resolution.

Technical Design & Direction

• Lead technical design for initiatives in your area and drive alignment across engineers and stakeholders.
• Identify and flag stack-level overlap or debt (e.g., duplicate tooling, conflicting frameworks) before it compounds.
• Set testing, rollback, and observability standards for the systems you own.

Mentorship & Review

• Review PRs from SDE-1s and give substantive, actionable feedback; mentor on technical depth and judgment.
• Keep documentation and runbooks for owned services current.
• Act according to priority — sequence own work and the team's backlog by business/technical priority, re-prioritizing quickly when priorities shift.

AI-Native Engineering

• Use AI coding assistants fluently to accelerate implementation, refactors, and test authoring — while owning review and correctness of the result.
• Design and defend a rollout/rollback strategy (shadow → canary → hot-swap) for AI-driven features you own.
• Design prompt, tool, and retrieval logic thoughtfully rather than treating the model as a black box to route around.

What Success Looks Like

• You consistently deliver systems that require little to no rework post-launch, including under ambiguity.
• You're a go-to reviewer and design partner whose input shapes how others build.
• You've driven at least one cross-team or cross-service initiative to completion with sound judgment on trade-offs and risk.
• Production issues in your owned systems are rare, and when they happen, you drive fast resolution.

Required Experience and Skills

• Solid full-stack capability plus deep expertise in at least one layer (frontend, backend, or data/infra).
• Hands-on experience designing agent/workflow orchestration, retrieval systems, or prompt/tool design for AI-powered features.
• Comfortable setting up observability/tracing and diagnosing issues independently.
• Track record mentoring junior engineers and reviewing code at a technical depth that raises the bar.
• Active, ongoing research into software engineering best practices and standard system design patterns, with the judgment to apply and adapt them to ConForge's systems.

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Role Summary

As the Construction Technology Specialist at ConForge AI, you bring firsthand civil and construction industry experience to the product — translating how construction companies actually work into clear product requirements and owning the product’s quality bar end-to-end using AI-assisted automated testing. You also own the evaluation, integration, and rollout of construction technology tools and platforms, from pilot through team-wide adoption.

Key Responsibilities

Business Use-Case Translation

• Understand construction business processes deeply — procurement, RFIs, submittals, scheduling, estimating, and field/site operations — from firsthand background and direct conversations with clients.
• Translate business use cases and stakeholder needs into clear product requirements, user stories, and acceptance criteria engineering can build against.
• Act as the voice of the construction end-user in product and sprint planning, sanity-checking that shipped features solve the real business problem, not just the literal spec.

Construction Technology Evaluation & Rollout

• Evaluate, select, and roll out construction technology tools (e.g., BIM coordination, drone/LiDAR site capture, jobsite IoT, project management platforms) supporting ConForge AI’s construction technology stack.
• Own evaluation, integration, and rollout end-to-end, from pilot through team-wide adoption.

Product Quality Ownership

• Own the quality bar for every release — hold authority to block a release on unresolved critical defects or a feature that doesn't hold up against a real construction scenario.
• Own release sign-off criteria from both a business-fit and a quality perspective, communicating risk clearly to the HOE before every release.
• Triage bugs and quality issues with enough context (repro steps, the real-world scenario they came from) that engineers can act without back-and-forth.

AI-Native Automated Testing

• Own the AI-native test strategy for the product: use AI tools (LLM-based test-case generation, self-healing UI test frameworks) to build and maintain automated test suites (UI, API, and construction-workflow-specific scenarios) integrated into CI/CD.
• Design evaluation sets for AI features from a domain-correctness angle — e.g., whether the agent cites the right RFI, applies to the right spec section, or gives an answer a real construction PM would trust.
• Treat AI-generated test cases as a draft to validate against real construction scenarios before adding them to the suite, not a final artifact.

Cross-Functional Partnership

• Own the AI-specific eval suite (gold-pair style test sets) alongside the data science function, with domain-correctness as the primary lens.
• Partner with the HOE and engineers on data ingestion/permissions design, ensuring product features respect the boundaries construction clients need.

What Success Looks Like

• Product requirements you write hold up under implementation with minimal re-scoping.
• Shipped features consistently match how construction teams actually work, with few post-launch "this isn’t how we do it" corrections.
• The automated test suite you own catches domain-specific issues (wrong RFI cited, stale spec version) that a generic functional test wouldn’t.
• Engineering trusts your release sign-off with confidence.

Required Experience and Skills

• Civil engineering or construction industry background — procurement, RFIs, submittals, scheduling, estimating, or field operations experience.
• Strong requirements-writing and stakeholder-translation skills; comfortable turning ambiguous business asks into clear acceptance criteria.
• Comfortable using AI coding/testing assistants to build and maintain automated test suites — no traditional SDE background required, but scripting/tooling literacy is expected.
• Working knowledge of prompt-based test generation and validating AI-generated test cases against real-world scenarios.
• Clear communicator, comfortable being the final voice on release quality and business fit.

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Reports to: Construction Technology Specialist

Role Summary

As the Construction Technology Intern at ConForge AI, you provide entry-level support to the Construction Technology Specialist, helping ramp up construction-tech initiatives without delay. You assist with tool evaluation, site data collection, and documentation while building familiarity with how construction workflows map to the product.

Key Responsibilities

Support & Execution

• Assist the Construction Technology Specialist with evaluation of construction technology tools (e.g., BIM coordination, drone/LiDAR site capture, jobsite IoT, project management platforms).
• Help collect, organize, and prepare site and construction data for evaluation and product use.
• Maintain clear documentation — tool comparisons, evaluation notes, site/meeting notes, and process write-ups.

Learning & Domain Fluency

• Build understanding of core construction workflows — procurement, RFIs, submittals, scheduling, estimating, and field operations.
• Learn how construction business processes translate into product requirements and test scenarios.

Quality & Testing Support

• Support the Specialist in validating features against real construction scenarios and in preparing and running test cases.
• Help draft and validate AI-generated test cases against real-world construction scenarios before they enter the suite.

AI-Native Practice

• Use AI tools for research, documentation, and test-case drafting, treating outputs as drafts to validate rather than final answers.

What Success Looks Like

• The Specialist can reliably delegate evaluation, data-collection, and documentation tasks to you.
• Documentation you produce is clear, current, and useful to the team.
• You steadily build construction-domain and product fluency, taking on more responsibility over time.

Required Experience and Skills

• Civil engineering / construction background or coursework, or a strong demonstrated interest in construction technology.
• Strong organization and documentation skills; careful and detail-oriented.
• Comfortable using AI assistants for research, documentation, and drafting.
• Eagerness to learn construction workflows and product/testing basics.
• Clear communicator; reliable and proactive in asking questions and raising blockers early.

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