AI Engineer
Location: Baku, Azerbaijan
Type: Full-time
Company: DRL LLC
About eiGroup
At eiGroup, we believe ideas can change industries - but only if they are nurtured with structure, science, and courage.
We’re an R&D and Innovation Venture Studio that transforms human ingenuity into technological value that scales.
Our ecosystem brings together researchers, engineers, and creators who turn complex challenges into scalable products - from subsurface imaging to AI-driven analytics, from remote sensing to digital transformation.
Our ventures are built in-house, born from research, and grown into independent companies.
Together, we’re shaping how innovation takes root in this region - and how it reaches the world.
What You’ll Do
LLM System Design & Deployment
- 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: design, version, and systematically evaluate 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.
RAG & Retrieval Systems
- Build and maintain RAG pipelines — including chunking strategy, embedding selection, vector store management, and retrieval evaluation — tuned for the application's domain.
- Work across retrieval approaches (dense vector search, BM25 hybrid, re-ranking) and evaluate trade-offs for accuracy, latency, and cost.
Agentic Workflows & Orchestration
- Select and apply frameworks (LangChain, LlamaIndex, LangGraph, custom) based on real trade-offs in the context of the product — not hype.
- Build with and extend MCP (Model Context Protocol) servers for tool integration, external service access, and structured agent communication.
Evaluation & Quality
- Define and run LLM evaluation pipelines — automated metrics, human eval, regression suites — and act on results without waiting for direction.
- Identify prompt regressions, retrieval quality issues, and latency problems early and drive resolution.
Collaboration & Engineering Culture
- Collaborate with backend and frontend engineers as a peer, translating AI capabilities into clean service contracts and integration specs.
- Identify architectural or data quality issues early and escalate when scope warrants.
- Stay current with the LLM ecosystem and bring concrete, well-reasoned proposals for adopting techniques or tooling that address real product problems.
- Contribute to technical documentation, internal best practices, and code reviews for junior team members.
What You Bring
Foundations
- 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.
AI / ML Expertise
- Practical experience building RAG pipelines: chunking, embedding models, vector stores (Pinecone, Weaviate, pgvector, Chroma), and retrieval evaluation.
- Familiarity with agentic frameworks and orchestration patterns: 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 an ability to go beyond the framework when needed.
- Awareness of prompt evaluation techniques: LLM-as-judge, embedding similarity, regression testing, and structured output validation.
Engineering Skills
- Strong data preprocessing skills: 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 containerization (Docker) and cloud deployment on Azure.
- Comfort working in a codebase with legacy components and the judgment to integrate cleanly without over-engineering.
Our benefits include:
- Medical insurance
- Flexible working hours
- Wellness program
- Childcare support
- Company-provided lunch
