Role Summary

We are seeking a contract AI Engineer (Generative AI) to design, build, and deploy practical GenAI solutions (e.g., document Q&A/RAG, structured extraction, copilots) in collaboration with product and engineering stakeholders. This role is hands-on and delivery-oriented, with a focus on implementing reliable services, evaluation, and clear documentation for maintainability.

Key Responsibilities

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.

Required Qualifications

3+ years experience in a data/ML/software engineering role with strong Python development skills.
Contract Basis - Renewable up to 2 years
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)
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

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