Senior AI Engineer — LLM & Agentic Systems

We are building an AI Engineering team focused on production-grade LLM systems. Not another chatbot demo, not a playground that never reaches users.

Our work sits between software engineering, applied AI, data systems, cloud infrastructure, evaluation, security and product thinking. We build LLM-powered systems that run on real business data, support real users and are designed to be monitored, evaluated, secured and maintained in production.

We need a Senior or Staff-level AI Engineer who can own the full path from problem understanding to production delivery. That means architecture, model and tooling choices, implementation and shipping.

This role is a strong fit for someone who likes pragmatic engineering, understands the limits of current LLMs and knows that a reliable AI system is much more than a prompt and an API call.

• RAG systems for unstructured and semi-structured data: documents, reports, tickets, knowledge bases and CRM/ERP records.
• Agentic workflows with tool calling, task planning, permissions, auditability and human-in-the-loop control.
• AI assistants for operational, analytical and product teams.
• Evaluation frameworks for answer quality, retrieval quality, prompt regressions and model behavior.
• Integrations between LLMs, internal APIs, data warehouses and business systems.
• Guardrails for sensitive data, hallucination control, prompt injection defense and traceability.
• Cost, latency, model selection, fallback and routing optimization.

• Design and develop production-grade LLM systems and agentic workflows.
• Build and optimize RAG pipelines: chunking, embeddings, retrieval, reranking, grounding and citation strategies.
• Design agent architectures: tools, memory, planning, routing, retries, timeouts, permission models and escalation paths.
• Integrate commercial and open-source models with applications, APIs and data systems.
• Create evaluation frameworks: offline evaluation, regression tests, test sets, LLM-as-judge and quality gates.
• Implement observability for AI systems: tracing, cost monitoring, latency monitoring, token usage, fallbacks and alerts.
• Work on AI security and governance: PII redaction, data access control, prompt injection defense and audit logs.
• Collaborate closely with Product, Data Science, Backend, Security and Cloud Engineering teams.
• Make technology decisions: when to use LangGraph, when to build simpler orchestration, when to use RAG, classic search, fine-tuning or a hybrid approach.
• Help shape AI Engineering standards: architectural patterns, repository templates, review checklists, documentation and delivery practices.

• Core engineering

  Python, FastAPI, Pydantic, SQL, PostgreSQL, Docker, GitHub Actions

• LLM / AI

  OpenAI / Azure OpenAI, Anthropic, open-source LLMs, Hugging Face, embeddings, rerankers, structured outputs, function calling

• Frameworks

  LangGraph, LangChain, LlamaIndex. Experience building lightweight custom orchestration instead of relying on frameworks by default is a strong plus

• RAG / search

  pgvector, OpenSearch / Elasticsearch, Qdrant, Pinecone, Weaviate, hybrid search, reranking, metadata filtering

• MLOps / LLMOps

  MLflow, Langfuse, LangSmith, OpenTelemetry, Prometheus, Grafana, test sets, golden datasets, evaluation pipelines

• Cloud / infrastructure

  AWS, Azure or GCP, Kubernetes, Terraform, serverless, CI/CD

• Data

  Snowflake, BigQuery, Databricks, Airflow, dbt or similar tools are a plus

• At least 5 years of commercial experience in software engineering, AI/ML engineering, data engineering or backend engineering.
• Strong Python skills and solid engineering fundamentals: testing, code review, modular design, clean architecture and CI/CD.
• Practical experience with LLM systems, RAG or agentic workflows that goes beyond local demos or notebooks.
• Experience with LangGraph, LangChain, LlamaIndex or the ability to consciously design a simpler alternative.
• Hands-on experience with vector databases, semantic search or hybrid search.
• Good SQL skills and experience working with production data.
• Ability to design systems that are reliable, observable and maintainable in production.
• Comfortable communication in English in an international environment.
• Ownership mindset. You care about the outcome, not just the ticket.

• Experience in regulated environments such as healthcare, fintech, insurance, legal, pharma or enterprise.
• Experience with MLOps / LLMOps and evaluation pipelines.
• Hands-on experience with Kubernetes, Terraform and public cloud platforms.
• Experience with model serving tools such as vLLM, TGI, Triton or similar.
• Understanding of LLM security risks: prompt injection, data leakage, access control and auditability.
• Experience with large document collections, knowledge graphs or GraphRAG.
• Previous experience as a Tech Lead, Staff Engineer or architecture owner.

• Clear cooperation terms and transparent workload expectations.
• Remote-first setup and flexible working hours.
• Budget for conferences, training and AI/cloud certifications.
• Equipment budget suitable for serious engineering work.
• Access to paid AI tools, test environments and experimentation budget.
• Time for research, prototyping and validating new approaches.
• Private medical care and benefits package.
• No unnecessarily long recruitment process.
• Participation in decisions about the AI roadmap, engineering standards and team direction.

We are looking for someone who understands that a production AI system is not just a prompt and an API call.

It is data, retrieval, evaluation, monitoring, cost, security, UX, deployment and long-term maintenance.

If you enjoy building AI systems that work beyond demo day, we should talk.