Principal Software Engineer designing, developing, and optimizing enterprise data solutions for WM. Leading complex projects and collaborating with cross - functional teams to align technical solutions with business needs.
About the role
- Senior Engineer developing Generative and Agentic AI capabilities at a global AI company. Collaborating hand-on with engineering and data science teams in an innovative environment.
Responsibilities
- Build and own production-grade agent systems end to end, from design through deployment and ongoing operation, including steady-state maintenance, reliability improvements, and operational support.
- Implement stateful, durable agentic workflows with clear checkpoints, safe retries, and human-in-the-loop steps for high-impact actions.
- Design agent architectures with planning, tool use, memory, and escalation, and proactively address common failure modes such as hallucinations, tool misuse, state errors, and loops.
- Build secure tool integrations using MCP-based connectors and/or tool registries to expose internal services and approved external SaaS APIs to agents.
- Implement advanced retrieval and grounding, including hybrid retrieval (vector plus structured), reranking, relevance tuning, and robust context assembly for grounded responses.
- Treat evaluation as an engineering discipline by creating offline datasets, regression gates, and online monitoring, and defining measurable success metrics such as task success, groundedness, and tool-call correctness that can gate releases.
- Instrument AgentOps and LLMOps by tracing full trajectories (retrieval, model calls, tool calls, outputs), attributing cost and latency per run, and alerting on drift and failure patterns using standard observability practices and tools.
- Develop self-improving and self-evolving agent loops using evaluator-driven optimization (generate, evaluate, refine) and/or RL-style approaches where rewards are verifiable (tests, constraints, correctness checks).
- Partner with cross-functional teams to translate problem statements into implementable AI solutions, and deliver components that fit broader architectures.
Requirements
- Experience: Typically 4 to 7 years in software engineering, applied ML, or applied AI, with demonstrated hands-on delivery ownership.
- Programming: Strong proficiency in Python; working experience with modern software engineering practices (testing, code reviews, version control).
- GenAI and LLM application development: Experience building LLM-powered applications, including prompt design, evaluation, and production considerations.
- Agentic frameworks: Practical experience with frameworks such as LangChain, LlamaIndex, Langfuse, LangGraph, CrewAI, or similar; understanding of agent planning, tool use, multi-agent collaboration.
- Retrieval systems: Hands-on experience with embeddings and vector databases (Milvus, Pinecone, Weaviate, Chroma, FAISS or similar), plus retrieval tuning and grounding patterns.
- AI architecture patterns: Understanding of microservices and event-driven architectures, ReAct, Self-Evolve and multi-agent systems.
- Cloud: Practical experience deploying services on at least one major cloud platform (Azure, AWS, GCP), including containerization and CI/CD.
- Production mindset: Familiarity with LLMOps practices such as automated testing, monitoring, governance, and operational readiness for LLM applications.
Benefits
- A competitive salary with a generous bonus
- private healthcare
- life assurance at 4 x your annual salary
- income protection insurance
- a rewarding pension
- professional and personal development opportunities
- support for a range of learning initiatives to enhance skills
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