Lead Generative AI Engineer

Cogent Labs is looking for a Lead Generative AI Engineer for Its Lahore office.

Job Type: On-site

Role:

The Lead GenAI Engineer is the department head responsible for all generative AI engineering at the company. This role combines technical leadership with people management—owning architecture decisions, technology standards, and the performance of 6-8+ GenAI engineers across multiple projects. They serve as the technical authority on GenAI solutions, participating in client discovery calls, leading technical demos, and designing solution architectures during pre-sales. While primarily focused on leadership and oversight, they remain hands-on when complex technical challenges require senior intervention. Reporting to the Head of Engineering, they collaborate closely with the Head of Delivery on capacity planning and resource allocation, and with GenAI Researcher(s) on emerging capabilities. They own the GenAI technology roadmap, tooling standards, and build vs. buy decisions that shape how the company delivers AI solutions.

Requirements (for an Ideal Candidate)

Experience:

• 5+ years of software engineering experience with at least 1.5-2 years focused on GenAI/ML
• Minimum 1 years in a lead or senior technical role with direct responsibility for a team
• Has shipped multiple GenAI solutions to production and led technical architecture for complex projects
• Technical Depth:

- Expert-level Python and software engineering fundamentals
- Deep understanding of LLM architectures, capabilities, and limitations
- Advanced experience with RAG systems, agentic frameworks (LangChain, LangGraph, PydanticAI), and autonomous agents
- Strong foundation in prompt engineering, context engineering, and evaluation strategies
- Production MLOps experience—deployment, monitoring, cost optimization
- Familiarity with fine-tuning, embeddings, vector databases, and MCPs
- Understanding of current GenAI landscape and emerging trends

• Architecture & Design: Proven ability to design scalable, maintainable AI systems. Can make pragmatic trade-offs between speed, quality, and cost.

Thinks beyond the immediate problem to long-term implications.

• Leadership & People Management: Has managed or led engineers before. Comfortable giving feedback, conducting reviews,

and having difficult conversations. Develops people, not just projects.

• Client-Facing Skills: Can lead technical discussions with non-technical stakeholders. Translates complexity into clarity.

Comfortable in discovery calls, demos, and solution presentations.

• Pre-Sales & Estimation: Experience contributing to technical scoping and estimates. Understands how inaccurate estimates create downstream delivery problems.
• Cross-Functional Collaboration: Works effectively with delivery, sales, and other engineering teams. Balances technical priorities with business needs.
• Strategic Thinking: Can define and execute a technology roadmap. Makes build vs. buy decisions with clear rationale.

Thinks about team capabilities, not just immediate project needs.

• Ownership Mentality: Takes responsibility for team outcomes, not just personal contributions. When something fails, focuses on fixing it and preventing recurrence.
• Communication: Clear and structured communicator—written and verbal. Can document decisions, present to leadership, and align stakeholders.

Responsibilities:

• Team Leadership & Management: Lead the GenAI engineering department. Conduct regular 1:1s, set clear expectations, and manage engineering performance. Own quarterly reviews for all GenAI engineers. Foster a culture of quality, ownership, and continuous learning.
• Performance Accountability: Accountable for the output and technical growth of all GenAI engineers. Identify skill gaps, provide coaching,

and ensure the team engineers consistently across projects. Address underperformance directly and develop improvement plans when needed.

• Technical Architecture Ownership: Own solution architecture for all GenAI projects. Define technical approach during discovery, review architectures before implementation, and ensure solutions are scalable, maintainable, and cost-effective.
• Pre-Sales & Discovery Participation: Join client discovery calls alongside sales. Lead technical discussions, assess feasibility, identify risks, and contribute to PRD development. Provide technical scoping and estimates that delivery can commit to.
• Technical Demos & Client Engagement: Lead technical demos and presentations to clients. Translate complex GenAI capabilities into business use cases. Serve as the senior technical voice in client-facing discussions.
• Multi-Project Oversight: Maintain visibility across all active GenAI workstreams. Execute strict & consistent technical standards including all code reviews, and intervene when projects face technical blockers or quality issues.
• Capacity Planning & Resource Allocation: Partner with Head of Delivery to forecast GenAI workload needs, allocate engineers to projects

based on skills and availability, and flag capacity constraints before they impact delivery.

• Technology Roadmap & Standards: Own the GenAI technology roadmap. Define and maintain coding standards, tooling choices, and best practices for the department. Evaluate emerging technologies and drive adoption where beneficial.
• Build vs. Buy Decisions: Assess and decide on AI tooling, infrastructure, and third-party services. Balance speed, cost, control,

and maintainability in technology choices.

• R&D Collaboration: Work closely with GenAI Researcher(s) to translate research findings into production-ready approaches.

Bridge the gap between experimentation and delivery.

• Hands-On Technical Contribution: Step in on complex technical challenges when senior expertise is required. Conduct architecture reviews,

debug critical issues, and pair with engineers on difficult problems.

• Cross-Functional Collaboration: Work with Delivery Managers, frontend, backend, and QA to ensure GenAI work integrates smoothly into overall project delivery. Align on priorities, timelines, and dependencies.
• Hiring & Team Growth: Participate in hiring for GenAI roles. Define role requirements, conduct final technical interview,

and make hiring recommendations. Build a team that can scale with business needs.

• Eval Coverage & Quality Assurance: Ensure evaluations are written for all AI work across projects. Drive adoption of eval frameworks to reduce manual testing, catch regressions early, and maintain output quality. Hold the team accountable for eval coverage as a standard practice, not an afterthought.
• AI-Driven Efficiency Improvements: Drive measurable improvements quarter over quarter in how AI is used in day-to-day work.

Identify opportunities to automate workflows, accelerate delivery, and reduce manual effort through AI tooling and practices. Track and report efficiency gains.

• Team Utilization & Efficiency: Minimize idle time across the GenAI team. Ensure engineers are allocated effectively across projects and internal initiatives. When project work is light, direct capacity toward R&D, internal tooling, or skill development. Maintain healthy utilization without burnout.

Results (What Success Looks Like)

• High-performing team — GenAI engineers consistently deliver quality work on time. The team is engaged, growing, and retention is strong.
• Reliable technical estimates — Pre-sales estimates are accurate. Projects don't blow up because discovery missed something or scope was underestimated.
• Consistent architecture quality — Solutions across projects follow standards, scale appropriately, and don't accumulate unnecessary tech debt.

No "wild west" implementations.

• Smooth resource allocation — Projects are staffed appropriately. No engineer is overloaded or idle. Capacity issues are flagged before they become delivery failures.
• Client confidence in technical leadership — Clients trust our GenAI expertise. Discovery calls and demos build credibility, not confusion.
• Adopted standards and best practices — The team follows established patterns. New engineers onboard faster because documentation and standards exist.
• Smart technology decisions — Build vs. buy choices pay off. The team uses tools that accelerate delivery without creating maintenance nightmares.
• R&D translates to delivery — Research insights make it into production. The company stays ahead technically because innovation is operationalized.
• No technical surprises — You know the health of every GenAI workstream. Problems surface early and get resolved before clients notice.
• Comprehensive eval coverage — AI solutions ship with evals in place. Manual testing burden is reduced. Regressions are caught automatically, not by clients.
• Demonstrable AI efficiency gains — Quarter over quarter, the team gets more efficient through AI adoption. Improvements are tracked and visible.
• Optimized team utilization — No engineer sits idle. Capacity is directed productively whether toward client work, R&D, or growth. Utilization is healthy and sustainable.

Job Type: Full-time

Pay: From Rs200,000.00 per month

Experience:

• Gen AI / ML: 1 year (Preferred)

Work Location: In person