About the role
Tasks
- Lead discovery and solution design for GenAI use cases, translating business problems into concrete architectures (LLM decision, RAGs, fine-tuning, agents, guardrails).
- Build end-to-end GenAI applications: data ingestion, retrieval layer, orchestration (e.g. LangChain/LlamaIndex/LangGraph), API/backend, and simple UI where needed.
- Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness.
- Perform model selection, prompting strategies, and fine-tuning (LoRA/QLoRA/SFT) for text, code, and multimodal models, including evaluation and A/B testing.
- Implement safety, compliance, and governance controls (input/output filters, PII handling, audit logs, human-in-the-loop review where required).
- Collaborate with data engineers, product owners, and full-stack developers on scalable architectures, SLAs, and integration with existing enterprise systems.
- Gather technical requirements and estimate planned work.
- Mentor other data scientists/engineers in GenAI patterns, code quality, and best practices; contribute to internal libraries, templates, and reusable components.
- Stay current with GenAI landscape (new open and hosted models, agentic frameworks, evaluation techniques) and perform targeted PoCs to validate them.
What We're Looking For
- 6+ years of experience in Data Science/AI engineering.
- At least 4+ years of experience in production-ready Python AI-related code development.
- At least 2+ years of experience in production-ready LLM-related code development, preferably based on the Retrieval-Augmented Generation (RAG) concept.
- Strong analytical and problem-solving skills with the ability to optimize AI solutions for diverse applications.
- Strong knowledge and experience in Generative AI, including LLMs, chatbots, AI agents, and RAG mechanisms.
- Deep understanding of LLM evaluators, validators, and guardrails.
- Hands-on experience with one or more GenAI frameworks: LangChain, LlamaIndex, LangGraph, or similar orchestration stacks.
- Hands-on experience designing or operating MCP servers/clients for LLM agents
- Strong Python skills, including production-grade code, packaging, and testing for data/ML services
- Solid understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model lifecycle, AI architectures.
- Proven ability to collaborate effectively across technical and non-technical teams.
- Familiarity with cloud environments such as Azure (preferred), GCP, or AWS, including AI-related managed services.
- Familiarity with CI/CD, testing, and containerized deployments.
- Excellent communication skills in English, with the ability to convey complex technical concepts to various audiences.
What Will Set You Apart
- Experience in designing and programming ML algorithms and data processing pipelines using Python.
- Good understanding of CI/CD and DevOps concepts, with experience working with selected tools (preferably GitHub Actions, GitLab, or Azure DevOps).
- Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes.
- Experience with agentic AI development frameworks (e.g., BMAD, multi-agent orchestration, spec-driven AI workflows).