Building and deploying AI agents and agentic workflows using the client's internal agent framework and modern orchestration tools (LangChain, LangGraph, CrewAI, AutoGen, or similar)
Designing and implementing RAG pipelines
including chunking strategies, embeddings, and vector store integrations (Pinecone, Weaviate, pgvector)
Processing and structuring financial documents (PDFs, DOCX reports, tables, CIM/DDQ materials) into clean, machine-readable outputs via Python
Integrating REST APIs and cloud services to connect agent workflows with existing business systems and data infrastructure
Owning the full delivery cycle for each initiative: scoping, development, testing, deployment, and handover to business users
Instrumenting agents for observability, writing test harnesses for non-deterministic behaviour, and ensuring failures are explicit and handled gracefully
Communicating proactively with the client-side team
surfacing blockers early, documenting solutions clearly, and keeping stakeholders in the loop without being asked

3-5 years of Python development experience
Hands-on experience building agentic AI workflows using LLM orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, or similar)
Experience with Claude API (Anthropic)
prompt engineering, tool use, multi-turn conversations, streaming
Practical knowledge of RAG pipeline design: chunking, embedding models, and vector stores
Experience with document processing: PDF/DOCX extraction, structured output generation, table parsing
Comfort integrating REST APIs and working across the stack
data transformation, prompt engineering, deployment
Strong written and spoken English (Upper-Intermediate minimum)
Proactive working style: raises issues early, communicates without prompting, treats documentation as part of deliverable
Experience in a regulated or security-sensitive environment, or demonstrable commitment to data integrity and auditability
Familiarity with Azure OpenAI or the Microsoft Azure ecosystem is a plus
Experience with V7 Go or other document-intelligence / PE-oriented AI platforms is a plus
Understanding of financial document structures: CIM, DDQ, LP reports, IC memos (domain awareness, not PE background required)

18 working days of fully paid vacation
10 compensated by 50% sick leaves without a note from the doctor
Opportunity to take study leaves
Different health insurance packages from partners
50% reimbursement for corporate English courses
Access to offline library
Large selection of corporate training and classes
Request for any internal or external training or certification if it applies to work responsibilities

AI Engineer

Key skills