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LLM Developer / AI Full Stack Developer

Roles & Responsibilities

  • Strong hands-on Python development (mandatory).
  • Experience with GenAI frameworks (LangChain, LangGraph, CrewAI).
  • Deep understanding of agentic reasoning, orchestration, and tool integration.
  • Experience with Azure AI Foundry and Azure OpenAI.

Requirements:

  • Design and implement end-to-end GenAI solutions using Python, including agentic workflows with LangGraph, tool calling, and multi-agent orchestration.
  • Develop robust RAG pipelines (ingestion, chunking, embeddings, vector search, re-ranking, grounding validation) and implement memory strategies (short-term, long-term, summarised, vector-based).
  • Integrate and evaluate multiple LLMs (e.g., GPT-4.x, GPT-4o) with structured outputs and tool-calling systems (schema enforcement).
  • Build evaluation frameworks (hallucination detection, quality scoring, latency, cost metrics) and implement Responsible AI guardrails (input/output filtering, fallback strategies); collaborate with DevOps/LLMOps for deployment, monitoring, scaling, and cost optimization.

Job description

We are looking for a Senior LLM Developer / AI Full Stack Developer in Lithuania or Latvia to work remotely for our client.

Key Responsibilities:

  • Design and implement end-to-end GenAI solutions using Python.
  • Build agentic workflows using frameworks such as LangGraph, tool/function calling, MCP, and multi-agent orchestration.
  • Develop robust RAG pipelines including ingestion, chunking, embeddings, vector search, re-ranking, and grounding validation.
  • Implement memory strategies (short-term, long-term, summarised, vector-based memory).
  • Integrate and evaluate multiple LLM models (e.g., GPT-4.x series, GPT-4o).
  • Develop structured output and tool-calling systems with schema enforcement.
  • Build evaluation frameworks covering hallucination detection, quality scoring, latency, and cost metrics.
  • Implement Responsible AI guardrails including input/output filtering and fallback strategies.
  • Collaborate with DevOps/LLMOps teams for deployment, monitoring, scaling, and cost optimisation.

Skills & Experience:

  • Strong hands-on Python development (mandatory).
  • Experience with GenAI frameworks (LangChain, LangGraph, CrewAI).
  • Deep understanding of agentic reasoning, orchestration, and tool integration.
  • Experience with Azure AI Foundry and Azure OpenAI.
  • Familiarity with vector databases (Azure AI Search, Pinecone, Weaviate, OpenSearch).
  • Understanding of tokenisation, context windows, rate limits, and cost optimisation.
  • API integration, async programming, and resilient error handling patterns.
  • Strong knowledge of Responsible AI risks and mitigation.

Other Requirements:

  • Production-first mindset (observability, resilience, scalability).
  • Ability to balance innovation with compliance and safety.

We offer:

  • Remote full-time work
  • B2B contract 
  • Rate: 33 - 35 EUR/h (negotible)

Recruitment Process:

  • CV Screening: Applications are reviewed within 24 hours.
  • Pre-Screening Interview: A brief Q/A session (Automated or with a Recruiter) designed to learn more about your experience related to the required job position.
    1. Automated Session (Recommended) – You can complete this session on your own at a time that is convenient for you. The questions and follow-ups are well-structured and designed to highlight your experience and provide detailed insights into your background. This option is recommended because it's usually more detailed and allows us to provide feedback from the hiring manager faster.
    2. Session with a Recruiter – You can also have the session with a recruiter. The questions are the same, but the discussion may be a bit less detailed, and feedback might take a little longer.
  • Shortlisting: Qualified candidates are presented to the hiring manager for review.
  • Formal Interviews: On-site discussions with the hiring manager or project team, with feedback provided within 1-2 weeks.
  • Offer and Onboarding: Successful candidates receive a formal offer and begin a structured onboarding process.

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