Gen AI engineer

Job Responsilities:

• Design and implement GenAI application workflows, including RAG pipelines (ingestion, chunking, embeddings, retrieval, prompting).
• Build structured extraction solutions (documents → fields/JSON) with validation logic and post-processing where required.
• Build and maintain API services (e.g., FastAPI/Flask) to expose GenAI capabilities for integration with internal systems.
• Create and maintain evaluation datasets and test harnesses (accuracy/consistency checks, regression tests, latency tracking).
• Conduct model and approach comparisons (LLM/embedding/retriever variants) and document trade-offs and recommendations.
• Apply engineering best practices: version control, reproducible environments, basic testing, logging, and error handling.
• Produce clear technical documentation: architecture notes, setup guides, runbooks, and handover materials.
• Collaborate with stakeholders to translate business needs into implementable user stories and deliver incrementally.

Job Requirements

Required Qualifications

• 3+ years experience in a data/ML/software engineering role with strong Python development skills.
• Practical experience delivering at least one of the following:
• Retrieval-Augmented Generation (RAG) using a vector database,
• LLM-based information extraction into structured outputs,
• Integration of LLMs via APIs for an end-user workflow.
• Experience building and consuming REST APIs and working with JSON schemas / structured outputs.
• Familiarity with evaluation concepts and metrics; able to implement repeatable testing for model quality.
• Comfortable working independently, communicating progress clearly, and iterating quickly based on feedback.

Preferred Qualifications

• Experience with vector stores (e.g., FAISS, Qdrant, Milvus, Pinecone) and retrieval tuning.
• Familiarity with local LLM tooling (e.g., Ollama) and/or cloud LLM platforms.
• Experience with document formats and parsing (PDF/XML/HTML), regex-based post-processing, and edge-case handling.
• Exposure to Docker, CI/CD, and basic observability/monitoring practices.
• Experience working with security/privacy requirements (PII handling, access controls).
• Experience with AWS cloud services (e.g., deploying services, using managed storage/compute, or integrating with AWS-native tooling).

Technical Skills

• Python (data handling, APIs, testing), SQL
• LLM/RAG concepts: embeddings, chunking strategies, retrieval, prompt templates
• API development (FastAPI/Flask), integration patterns
• Basic software engineering practices (Git, documentation, reproducibility)

Soft Skills

• Strong problem-solving and debugging ability
• Clear written and verbal communication with technical and non-technical stakeholders
• Ability to manage priorities and deliver outcomes in a time-boxed environment.