Engineering Lead – Applied AI Professional Services Team

Experience : 6+ Years 

About the Role

As an Engineering Lead at Lyzr, you will own the execution, quality, and scalability of the GenAI platform and customer-facing agentic systems.

This is a hands-on leadership role at the intersection of backend engineering, distributed systems, and GenAI infrastructure. You will lead engineers building core platform primitives such as agent orchestration, RAG pipelines, LLM integrations, and observability systems.

You will work closely with Solutions Architects, Product Managers, and Customers to ensure GenAI architectures are translated into systems that ship, scale, and remain stable in production.

What You’ll Do

• Lead engineers building Lyzr’s GenAI platform and agent infrastructure using Python and FastAPI, with a strong focus on correctness, performance, and maintainability.

• Own end-to-end delivery of platform and customer-facing capabilities, from technical design and implementation through testing, deployment, and production operations.

• Translate high-level GenAI and agentic architectures into scalable backend systems, making pragmatic trade-offs between flexibility, reliability, and time-to-market.

• Review, influence, and take ownership of system architecture, data models, API contracts, and critical code paths across services.

• Design, build, and scale multi-agent orchestration systems, including tool execution, memory management, state handling, and long-running workflows.

• Implement and evolve production-grade Retrieval-Augmented Generation (RAG) pipelines, covering ingestion, chunking, indexing, retrieval, re-ranking, and context assembly.

• Design and maintain APIs for agents, tools, microservices, and external system integrations, ensuring clear contracts and backward compatibility.

• Optimize LLM usage across the platform for latency, throughput, cost efficiency, and reliability, including model selection, request shaping, and caching strategies.

• Build strong observability into GenAI systems, including structured logging, metrics, tracing, and alerting to diagnose failures and performance issues.

• Own incident response for production systems, drive root cause analysis, and implement long-term reliability and resilience improvements.

• Partner closely with Solutions Architects to validate technical feasibility, execution plans, and delivery risks for customer and platform initiatives.

• Collaborate with Product Managers to shape the roadmap, balancing product ambition with engineering rigor and sustainable delivery.

• Mentor engineers through design reviews, code reviews, and day-to-day guidance, raising the overall engineering bar across the team.

• Implement secure data handling, access controls, and compliance mechanisms to support enterprise AI use cases, including PII-sensitive workflows.

What You Need to Excel:

Years of experience:

• 7+ years of experience in backend, platform, or distributed systems engineering.

• 2+ years of experience in a technical leadership role such as Engineering Lead, Tech Lead, or Senior Engineer, owning system-level decisions.

• Strong sense of accountability, ownership, and product expertise.

Proven experience delivering:

• Backend or platform systems operating at production scale in a SaaS or enterprise environment.

• GenAI-powered systems such as agents, chatbots, copilots, or workflow automation tools.

• RAG-based applications leveraging vector databases and embedding pipelines.

• APIs and microservices consumed by internal teams, frontend applications (React / Next.js), and external enterprise customers.

• Highly available systems with strong observability, monitoring, and operational discipline.

• Strong understanding of:

  • Backend system design, distributed systems, and API architecture.

  • Python-based backend development, preferably using FastAPI.

  • LLMs, embeddings, prompting strategies, and tool-calling mechanisms.

  • Vector databases such as Qdrant, Weaviate, Chroma, or MongoDB-based vector search.

  • Data stores including MongoDB and relational databases.

  • Cloud-native architectures on AWS, including containerized and managed services.

  • Performance tuning, scalability, and cost optimization for GenAI workloads.

  • Security, privacy, and compliance considerations for enterprise AI systems.

Nice to Have

• Experience building developer platforms or internal infrastructure products.

• Exposure to frontend systems (React / Next.js) and full-stack collaboration patterns.

• Startup or zero-to-one product development experience.

• Experience with Kubernetes or large-scale distributed systems.

• Familiarity with LLM evaluation frameworks and human-in-the-loop systems.

• Prior experience leading or mentoring fast-growing engineering teams.

Why Lyzr

• Build the core infrastructure behind real-world, production-grade agentic AI systems.

• Solve challenging engineering problems at the intersection of GenAI and distributed Systems.

• High ownership with deep technical and architectural influence.

• Direct impact on how enterprises adopt, deploy, and govern AI agents.

• Work closely with strong product, architecture, and engineering leaders