Why this role exists:

We are growing our AI team and are looking for a Senior AI / Generative AI Engineer to build and operate production-grade LLM-based systems across the platform.

This role focuses on applied Generative AI: internal AI automation, chatbots, AI assistants, and MCP-based services already used in production. You will work on top of an existing ML and data platform, building systems that are reliable, maintainable, and practical to operate.

This is a hands-on engineering role with end-to-end ownership. Not research. Not prompt-only.

What you’ll drive:

Technical Ownership

• Own LLM-based systems from design to production support.
• Design architectures for Generative AI systems (RAG, agents, tool/context serving).
• Make clear trade-offs between quality, latency, cost, and complexity.
• Set engineering standards for building and operating AI systems in production.
• Act as a technical reference point for applied GenAI.

Hands-on AI Engineering

• Build and maintain production LLM-powered systems.
• Develop chatbots and AI assistants with predictable behavior.
• Design and implement RAG pipelines (ingestion, embeddings, retrieval, generation).
• Implement agent-style workflows with tool/function calling.
• Build and integrate MCP servers or similar context/tool-serving components.
• Integrate AI systems with backend services and ML infrastructure.

Production & Reliability

• Design evaluation frameworks for LLM outputs.
• Monitor LLM behavior, system health, and costs in production.
• Address performance, scalability, and operational issues.
• Handle production incidents and contribute to long-term fixes.
• Design systems with failure modes, fallbacks, and graceful degradation.

What makes you a GR8 fit

Must-have

• Strong software engineering background.
• Proven experience building LLM-based systems in production.
• Hands-on experience with: LLMs and RAG architectures, Chatbots or conversational AI systems, Tool/function calling and agent-style patterns.
• Experience with context or tool-serving systems (MCP or similar).
• Experience integrating AI into real-time production products.
• Understanding of performance, scalability, and cost trade-offs.
• Cloud experience (preferably AWS).
• Strong engineering fundamentals (testing, debugging, reviews, observability).

Nice-to-have

• Multi-agent or workflow-based systems.
• Fine-tuning or adapting open-source LLMs.
• ML platforms / MLOps background.
• Experience operating latency- or cost-sensitive systems.
• Production incident ownership in AI systems.

Tech Stack:

• Languages: Python, SQL.
• LLMs: Amazon Bedrock, OpenAI, Anthropic, open-source models.
• Frameworks: LangChain, LangGraph, Pydantic, Langfuse.
• RAG: Embeddings, Qdrant / FAISS / OpenSearch / pgvector.
• Cloud: AWS (ECS / EKS / Lambda, S3, OpenSearch).
• DevOps: Docker, Git, CI/CD.