Engineering Manager AI

Responsibilities:

• Lead and manage engineering teams building cloud-native, agentic AI systems and retail decision intelligence platforms.
• Architect multi-agent orchestration, hybrid RAG pipelines, dynamic tool execution, state/memory management, and structured planning workflows.
• Own the design, performance, and reliability of Python-based microservices, PostgreSQL databases, and complex SQL data pipelines.
• Establish evaluation frameworks (evals) to measure agent accuracy, reasoning fidelity, hallucination rates, latency, and token cost-efficiency.
• Implement robust AI safety guardrails, human-in-the-loop (HITL) intervention points, prompt-injection defences, and secure sandboxing.
• Drive observability, monitoring, alerting, and APM practices (metrics, logs, distributed tracing) across high- throughput production services.
• Partner with DevOps on cloud infrastructure (GCP), CI/CD pipelines, containerization, and production release readiness.
• Collaborate with Product Management, Data Science, and Architecture teams to translate complex retail business problems into scalable engineering roadmaps.
• Coach, mentor, and hire top-tier software and AI engineers while maintaining high engineering quality and agile delivery.

Requirements:

• 14-17 years of total software engineering experience, including significant experience directly leading and managing engineering teams.
• Hands-on technical leadership expertise in Python (or Java/C++) alongside PostgreSQL and advanced SQL optimisation.
• Proven experience building and deploying production-grade LLM applications, autonomous agents, or multi- agent orchestration frameworks (e. g., LangGraph, AutoGen, CrewAI, LlamaIndex).
• Deep practical knowledge of Observability, APM tools, distributed tracing, and root-cause analysis for microservices.
• Strong experience with cloud platforms (experience on GCP is a strong plus), REST/gRPC APIs, and event-driven architectures.
• Strong experience driving Agile execution, including sprint planning, backlog grooming, retrospectives, and delivery tracking.
• Superior verbal and written communication skills with the ability to clearly present technical concepts to executive leadership and business stakeholders.
• Strong ownership mindset with attention to platform availability, performance, and high enterprise reliability.

Good to have:

• Experience fine-tuning open-source LLMs (e. g., Llama, Mistral) for tool calling, function execution, or reasoning tasks.
• Familiarity with AI safety suites (e. g., NeMo Guardrails, Llama Guard) and vector databases (Pinecone, Qdrant, Milvus).
• Exposure to frontend technologies like ReactJS and modern web architectures.
• Experience in the retail domain, especially analytics, forecasting, pricing, assortment, or supply chain systems.