Translate product and engineering challenges into AI-driven solutions that enhance speed, quality, and outcomes
Build and deploy AI Agents with advanced reasoning, integrating memory, MCP, custom MCP servers, and A2A
Apply prompt engineering, context engineering, AI steering, RAG, Chain of Thought, ReAct, and other modern AI frameworks to real-world use cases
Partner with product and engineering teams to embed AI, LLMOps, and observability into requirements, coding, testing, monitoring, and operations
Prototype, test, optimise, fine-tune, and scale AI solutions, balancing experimentation with production readiness and inference deployment
Design, run, and automate evals to test LLM outputs for quality, reliability, and safety
Implement security guardrails and robust data integration across agentic workflows to mitigate vulnerabilities
Support pre-sales and client discussions by demonstrating applied AI use cases and outcomes
Stay ahead of research and practice in GenAI and bring them into daily engineering practice
Communicate findings and trade-offs clearly to both technical teams and executives
Required qualifications:
Bachelor's Degree in Computer Science, Engineering, Applied Math, or related fields
Strong programming background in Python (or similar) with experience in GenAI frameworks and APIs
Daily use of Generative AI IDEs or environments
Proven experience in Prompt Engineering, Context Engineering, AI Steering, RAG, MCP (Model Context Protocol) and building agent workflows
Solid experience with LLMOps, structured Evals, and LLM observability/tracing tools
Proven knowledge of GenAI Security practices (guardrails, prompt injection mitigation) and secure data integration
Solid understanding of A2A (Agent to Agent) and ACP (Agent Coordination Protocol)
Experience in deploying AI-powered solutions across the product development lifecycle, from design to monitoring
Understanding of how to integrate short and long-term memory in agents
Strong communication skills in English, both technical and business-oriented
Exposure to cloud native environments
Ability to work independently and collaboratively in fast-paced environments
Desired qualifications:
Knowledge of reasoning strategies (Chain of Thought, ReAct)
Experience with Agentic AI frameworks, autonomous agents and Multi-Agent Orchestration frameworks (e.g., LangGraph, CrewAI)
Hands-on experience with LLM optimisation, Fine-Tuning techniques, and production inference deployment
Experience developing custom MCP (Model Context Protocol) servers to connect agents with external tools and data
Experience designing and applying evals to validate LLM outputs
Experience with Knowledge Graphs, or hybrid RAG approaches
Experience monitoring AI systems for performance, accuracy, and cost
Collaboration is our superpower, diversity unites us, and excellence is our standard.
We value diverse identities and life experiences, fostering a diverse, inclusive, and safe work environment. We encourage applications from diverse and underrepresented groups to our job positions.