Lead Engineer, Agentic AI Engineering

About Trinity & This Role

Trinity Life Sciences is a leading analytics and technology partner to the biotech and pharma industry, helping clients solve their hardest commercialisation challenges across market access, forecasting, field force, customer engagement, and real world evidence. AI Foundations is the internal team making AI adoption fast, safe, and repeatable across Trinity — building the platforms, standards, and reusable components that product teams build on.

Trinity is seeking a Lead AI Engineer – Agentic Systems to take ownership of technical execution across the AI Foundations engineering team in India. This is a step up from Senior Engineer: you are still deeply hands-on, but you carry additional responsibility for how the team executes — ensuring deliverables land on time and to standard, coaching engineers on agentic and platform development approaches, and acting as the technical authority the team rallies around. The right candidate leads by doing, holds the bar high, and understands that their success is measured as much by the team's output as their own.

Key Responsibilities

Team Oversight & Project Execution

This is the dimension that distinguishes the Lead role from Senior. You are responsible for how the team delivers, not just what you personally build.

·       Own end-to-end delivery of agentic AI engineering workstreams — planning, prioritisation, dependency management, and ensuring deliverables complete on time and to the quality standard the business expects.

·       Track progress across parallel workstreams; surface blockers early, escalate when needed, and remove obstacles so engineers can focus on building rather than navigating process.

·       Maintain visibility into the health of ongoing projects: scope creep, technical debt accumulation, integration risks, and resource constraints — and act before they become problems.

·       Coordinate with Data Scientists, product managers, and stakeholders to align on requirements, timelines, and definition of done — ensuring engineering and business expectations stay in sync throughout delivery.

·       Run effective sprint rituals, design reviews, and retrospectives that keep the team sharp and continuously improving without adding unnecessary overhead.

Architecture Standards & Technical Governance

·       Act as the technical authority for agentic systems architecture within the India team — reviewing and approving significant design decisions, ensuring consistency with Trinity's established patterns and guardrails.

·       Enforce adherence to engineering standards: coding conventions, testing requirements, CI/CD practices, security and RBAC patterns, and agent harness implementation — not as bureaucracy, but as the foundation that makes the team go faster over time.

·       Own the team's reference architectures and reusable component library; keep them current as the agentic AI landscape evolves and new approaches prove themselves.

·       Identify and address technical debt proactively — make the call on when to refactor versus when to ship, and be transparent with stakeholders about the trade-offs.

Hands-On Engineering

The Lead role is not a management role. You remain a significant individual contributor and the technical credibility that comes from that is what makes the rest of the role work.

·       Architect and deliver complex agentic AI solutions end-to-end — multi-agent systems on LangGraph, AutoGen, CrewAI, Google ADK, and platforms including AWS Bedrock, Azure AI Foundry, Snowflake Cortex, and Databricks Mosaic AI.

·       Design for MCP/A2A interoperability, agent memory systems, tool registries, and human-in-the-loop checkpoints; implement agent execution harnesses with sandboxed tool execution, policy-as-code guardrails (NeMo Guardrails, Guardrails AI), and full observability via Langfuse or LangSmith.

·       Build or integrate with full-stack enterprise applications — applying enterprise-grade security (OAuth2, SAML, SSO, RBAC), secrets management, audit logging, and secure API design as first-class engineering concerns.

·       Make informed, production-tested choices on inference infrastructure (vLLM, SGLang, TensorRT-LLM), container orchestration (Docker, Kubernetes / EKS / AKS), and data tooling (SQL, Snowflake, Databricks).

Team Growth, Mentorship & Culture

·       Coach and mentor engineers across all levels on agentic AI development, platform engineering, and software engineering craft — investing in their growth as seriously as in the team's output.

·       Run structured technical sessions: design reviews, architecture walkthroughs, code review standards, and knowledge-sharing forums that build collective capability in agentic and platform development.

·       Identify skill gaps in the team and work with leadership to address them — through hiring, coaching, or deliberate project assignments that stretch engineers in the right directions.

·       Foster a culture of ownership, high standards, and continuous improvement; be the kind of technical leader people want to work for and learn from.

·       Contribute actively to hiring — define what good looks like for engineering roles, assess candidates rigorously, and help build a team that makes everyone in it better.

Stay Current & Drive Adoption of What Works

·       Stay ahead of the agentic AI field — evaluate new frameworks, protocols, and techniques with a critical eye and bring well-formed recommendations to the team on what is worth adopting.

·       Champion the adoption of AI-enabled development practices that improve team productivity: code generation, automated testing, documentation tooling, and developer experience improvements.

What We Are Looking For

·       8+ years of software engineering in production, with 4+ years in AI/ML or agentic systems — and at least 2 years in a technical lead or principal engineering capacity.

·       Proven ability to drive delivery across a team: you have owned project timelines, managed dependencies, and ensured engineering work lands on time and to standard.

·       Deep technical expertise across the agentic AI stack — frameworks, cloud platforms, agent safety, evaluation, inference infrastructure, and full-stack enterprise integration.

·       Strong Python and SQL proficiency; hands-on experience with Docker and Kubernetes in production container environments.

·       Demonstrated ability to set and uphold engineering standards — coding, testing, architecture, and security — and bring a team along with you, not just impose them.

·       Excellent communication: able to represent technical decisions to senior leadership, align with business stakeholders, and coach engineers at every level.

·       Passion for the agentic AI field and a genuine habit of staying current — you bring ideas to the team, not just execution.

·       Background in life sciences, pharma, or regulated environments is a strong differentiator. Degree from IITs, NITs, BITS, or comparable; strong applied track record considered equally.

The Kind of Achievements We Respect

·       "Led a 4-person engineering team to deliver a multi-agent analytics platform on AWS Bedrock in 12 weeks. All milestones hit. Adopted by 3 product teams within a month of launch."

·       "Introduced architecture review and testing standards across the team. Deployment-related incidents dropped by 70% in the following quarter."

·       "Coached two engineers from mid-level to senior in 18 months through structured design reviews, paired engineering sessions, and deliberate project ownership."

Why This Role Matters

Trinity is building something foundational in India — a team that will shape how AI gets built and adopted across the enterprise. The Lead Engineer is the person who makes that team deliver with consistency and craft. If you are energised by the combination of deep technical work, team ownership, and genuine influence over how an AI organisation develops, we would like to meet you.