Lead AI Engineer

Responsibilities:

• Design and ship AI-powered product features (LLMs, RAG, agents, ML APIs) into our existing services, working closely with backend, frontend, and data science teams.
• Integrate off-the-shelf and in-house models (LLMs, embeddings, ML APIs) into robust microservices and user-facing flows.
• Design and implement RAG and workflow/agent pipelines: retrieval, context assembly, tools integration, guardrails, and fallbacks.
• Own AI service reliability in production: latency, throughput, cost, observability, circuit breakers, and rollback/versioning of models and prompts.
• Collaborate with data scientists who own model training/fine-tuning and evaluation design; productionise their outputs as stable APIs/workflows.
• Implement logging, feedback capture, and lightweight online evaluation hooks to measure the quality of AI features over time.
• Ensure safety, security, and compliance for AI features: prompt injection defences, PII handling, abuse/hallucination controls, and audit trails.
• Contribute to internal AI tooling: SDKs, templates, and reusable components to accelerate future AI use cases.

Requirements:

• Strong software engineering in Python (and one of Node/Java/Go), REST/gRPC APIs, queues, and microservices on cloud infrastructure.
• Hands-on experience shipping at least one AI-powered product to production (e. g., search, recommendations, chatbots, summarisation, and classification).
• Practical knowledge of LLM concepts: prompts, context engineering, embeddings, vector search, basic evaluation metrics, and latency/cost trade-offs.
• Solid understanding of integration patterns with third-party AI providers (OpenAI, Anthropic, etc. ) and vector DB.
• Hands-on and good understanding of at least one agentic framework like LangGraph.

Skills required:

• The AI Engineer role demands more than AI-based augmentation with an in-depth understanding of concepts like RAG, GenAI, LLM finetuning, prompt engineering, multi-agent frameworks (LangChain, LangGraph, etc., with hands-on experience), eval generation and its importance, token usage and optimisations, and model/MCP gateways.