Senior Artificial Intelligence Engineer

Role summary

We’re looking for a hands-on Senior AI Engineer to design, build, and ship production-grade LLM applications, including RAG systems and tool-using agents. You’ll work with Product, Architecture, and Engineering to take AI use cases from proof of concept to reliable, cost-effective production solutions.

Responsibilities

• Design, build, and deploy production LLM applications, from prototype through monitoring and iteration.
• Take end-to-end ownership of assigned AI features, from technical approach through delivery and production support.
• Build and optimize RAG pipelines: ingestion, parsing, chunking, embeddings, indexing, retrieval, reranking, and grounded generation.
• Develop AI agents with tool calling, multi-step reasoning, and integration with external APIs and enterprise systems, including through MCP.
• Apply prompt and context engineering, structured outputs, and model selection to improve quality.
• Build evaluation frameworks to measure accuracy, retrieval relevance, groundedness, hallucination, and regressions.
• Optimize for latency, cost, scalability, and reliability.
• Develop backend services and REST APIs in Python (FastAPI or Flask).
• Communicate AI capabilities, limitations, and trade-offs clearly to technical and non-technical stakeholders, including clients; write design notes and documentation, and present demos.
• Mentor junior engineers through code reviews, pairing, and knowledge sharing, while remaining a hands-on individual contributor.
• Collaborate with Product and Architecture to translate business needs into scalable solutions, and contribute reusable components and engineering standards.

Required qualifications

• 7+ years of industry experience, including 2+ years building LLM-powered applications.
• Strong Python skills and experience building production backend services and APIs.
• Strong understanding of LLM fundamentals, including transformer architecture and attention, tokenization and context windows, embeddings and semantic similarity, decoding parameters (temperature, top-p), causes of hallucination, and when to use prompting, RAG, or fine-tuning.
• Hands-on experience with RAG architectures and at least one vector database (pgvector, Pinecone, Qdrant, Weaviate, Chroma, or similar).
• Hands-on experience integrating LLMs into applications through APIs (OpenAI, Anthropic, Gemini, AWS Bedrock, Azure OpenAI) or open-weight models, including function calling and structured outputs.
• Experience building AI agents or tool-calling workflows.
• Hands-on experience with LangGraph (and familiarity with LlamaIndex or LangChain), including multi-agent design.
• Working knowledge of Model Context Protocol (MCP), including building or consuming MCP servers to connect agents to tools and data sources.
• Experience evaluating LLM applications (e.g., retrieval metrics, LLM-as-a-Judge, regression testing).
• Awareness of LLM security risks such as prompt injection and data leakage, and experience implementing guardrails and access controls for agents and tools.
• Working knowledge of AWS or an equivalent cloud (e.g., EC2, ECS, Lambda, S3), Docker, CI/CD, and production monitoring, including secure handling of sensitive data.
• Excellent written and verbal communication skills, with the ability to explain complex AI concepts and set realistic expectations on accuracy and behaviour.
• Ability to design, build, and debug end-to-end solutions independently.

Good to have

• Hybrid search, reranking, and query transformation.
• AI gateway architectures.
• Document intelligence: PDF processing, OCR, information extraction.
• AI observability, model routing, and PII protection.
• Background in ML/deep learning, and model fine-tuning (e.g., LoRA/QLoRA).
• Open-source model serving (Ollama, vLLM).
• Experience in regulated domains (healthcare, HRTech, fintech, insurance)