Senior AI Platform Engineer

About the Opportunity

We are partnering with an innovative technology company that is building next-generation AI solutions designed to transform complex unstructured information into actionable business intelligence.

This is an opportunity to join a highly technical team focused on delivering production-grade AI systems that combine large language models intelligent agents knowledge retrieval and enterprise software engineering.

We are looking for a Senior AI Platform Engineer who enjoys solving complex engineering challenges and building reliable AI-powered applications that users depend on every day.

The Role

As a Senior AI Platform Engineer you will take ownership of the architecture and development of advanced AI services including agent orchestration retrieval-augmented generation (RAG) tool integration frameworks and structured reasoning systems.

This is a hands-on engineering role rather than a research position. Success requires strong expertise in both modern AI application development and backend software engineering.

You will help design systems that are scalable observable cost-efficient and robust enough for enterprise production environments.

What Youll Be Doing

Build Intelligent Agent Systems

• Design and implement multi-agent architectures capable of planning execution validation and human-assisted decision-making.
• Develop complex workflows using LangGraph LangChain or custom orchestration frameworks.
• Create reliable tool-calling infrastructures that connect AI agents to external services and business systems.
• Develop memory and context-management strategies for long-running agent interactions.

Develop Advanced RAG Solutions

• Design and optimize retrieval-augmented generation pipelines.
• Work with vector databases and embedding technologies to improve knowledge retrieval performance.
• Implement advanced retrieval techniques including hybrid search reranking query decomposition and citation-based grounding.
• Build scalable ingestion and indexing pipelines for large knowledge repositories.

Deliver Reliable AI Outputs

• Design systems that combine deterministic software logic with LLM-powered reasoning.
• Implement robust validation frameworks using Pydantic JSON Schema and structured output patterns.
• Create safeguards and quality controls that ensure consistent production-ready outputs.

Drive AI Quality & Performance

• Develop testing and evaluation frameworks for AI-powered applications.
• Monitor quality latency cost and user experience metrics.
• Build benchmarking regression testing and A/B testing capabilities.
• Optimize prompts model selection strategies caching and token consumption.

Engineer Production-Ready Platforms

• Build and maintain APIs using FastAPI and modern Python frameworks.
• Implement real-time and streaming AI experiences.
• Work with PostgreSQL Redis Elasticsearch and vector databases.
• Collaborate on containerized deployments using Docker and Kubernetes.
• Support the full software lifecycle from architecture through production operations.

What Were Looking For

Essential Skills & Experience

• 4 years of professional software engineering experience.
• 2 years building production AI or LLM-powered applications.
• Strong Python development skills with experience building scalable backend services.
• Hands-on experience with FastAPI or similar asynchronous web frameworks.
• Proven expertise in agentic AI frameworks such as LangGraph LangChain or custom orchestration systems.
• Experience designing and deploying RAG architectures.
• Deep understanding of prompt engineering structured outputs model selection and LLM optimisation.
• Strong experience with Pydantic and schema-driven development.
• Solid database knowledge including PostgreSQL and Redis.
• Experience working with vector databases such as Qdrant Pinecone Weaviate FAISS or pgvector.
• Experience building automated testing monitoring and evaluation systems for AI applications.
• Strong understanding of API design WebSockets and distributed systems.
• Excellent debugging and problem-solving skills.

Nice to Have

• Experience with Model Context Protocol (MCP).
• Knowledge of fine-tuning techniques such as LoRA or QLoRA.
• Experience building multi-tenant SaaS platforms.
• Familiarity with AI observability tools such as LangSmith or Langfuse.
• Experience implementing hybrid search solutions using Elasticsearch.
• Cloud platform experience particularly AWS.
• Contributions to open-source AI projects or published technical work.
• Experience working on enterprise AI platforms or knowledge management systems.

Why This Opportunity

• Work on cutting-edge agentic AI systems solving real business problems.
• Build production AI applications rather than experimental prototypes.
• Influence architecture and technical direction from an early stage.
• Collaborate with highly experienced engineers and AI specialists.
• Work with modern technologies across LLMs agents RAG cloud infrastructure and distributed systems.
• Join a company investing heavily in AI innovation and enterprise-scale platforms.