ai engineer for conversational AI

eiGroup is an R&D and Innovation Venture Studio that transforms research and human ingenuity into scalable technological products. Its ecosystem brings together researchers, engineers, and creators to develop ventures in areas including subsurface imaging, AI-driven analytics, remote sensing, and digital transformation.

• Design and implement LLM-powered features end-to-end, from prompt architecture and model selection through API integration and production deployment, with minimal supervision;
• Own prompt engineering for production features by designing, versioning, and systematically evaluating prompts across model updates and behavior regressions;
• Integrate conversational and agentic AI capabilities into an existing application, owning the API layer, session management, and graceful degradation strategies;
• Build and maintain RAG pipelines, including chunking strategy, embedding selection, vector store management, and retrieval evaluation, tuned for the application's domain;
• Work across dense vector search, BM25 hybrid search, and re-ranking approaches, evaluating trade-offs for accuracy, latency, and cost;
• Select and apply LangChain, LlamaIndex, LangGraph, custom, or other frameworks based on product-specific trade-offs;
• Build with and extend MCP servers for tool integration, external service access, and structured agent communication;
• Define and run LLM evaluation pipelines, including automated metrics, human evaluation, and regression suites, and act on results without waiting for direction;
• Identify prompt regressions, retrieval quality issues, and latency problems early and drive their resolution;
• Collaborate with backend and frontend engineers as a peer, translating AI capabilities into clean service contracts and integration specifications;
• Identify architectural or data quality issues early and escalate when scope warrants;
• Stay current with the LLM ecosystem and propose techniques or tooling that address real product problems;
• Contribute to technical documentation, internal best practices, and code reviews for junior team members.

• BSc or MSc in Computer Science, Machine Learning, AI, or a related field;
• At least 1–2 years of hands-on experience in LLM engineering through industry, coursework, or substantive personal projects;
• Solid understanding of transformer-based LLM architectures and how model behavior, context windows, and inference parameters affect output;
• Practical experience building RAG pipelines with chunking, embedding models, vector stores, and retrieval evaluation;
• Familiarity with agentic frameworks and orchestration patterns, including tool use, memory systems, multi-step reasoning, and agent-to-agent communication;
• Understanding of MCP (Model Context Protocol) for building interoperable tool integrations and structured agent workflows;
• Experience with LLM tooling such as LangChain, LlamaIndex, LangGraph, or equivalent, with the ability to go beyond the framework when needed;
• Awareness of prompt evaluation techniques, including LLM-as-judge, embedding similarity, regression testing, and structured output validation;
• Strong data preprocessing skills, including regex, normalization, pipeline design, and working with messy real-world data;
• Proficiency in Python, with exposure to REST API design and async patterns;
• Familiarity with Docker containerization and cloud deployment on Azure;
• Comfort working in a codebase with legacy components and integrating cleanly without over-engineering.

• Medical insurance;
• Flexible working hours;
• Wellness program;
• Childcare support;
• Company-provided lunch.