GenAI Python Engineer/Hybrid

Design and build GenAI/RAG-based applications using data from multiple enterprise systems.

Integrate with structured and unstructured data sources, including databases, APIs, files, and document repositories.

Develop pipelines to parse and extract data from documents and images using OCR, document intelligence, and related tools.

Process and structure extracted content for downstream AI use cases.

Store parsed and chunked content in a vector database and manage embeddings effectively.

Implement and optimize retrieval pipelines, including chunking, indexing, metadata tagging, filtering, and reranking.

Build workflows to interact with relational and enterprise databases for querying and enrichment.

Ensure the application follows strong evaluation practices, including accuracy, groundedness, relevance, hallucination checks, and response quality against ground truth.

Work closely with architects, platform teams, and business stakeholders to deliver scalable and secure solutions.

Seeking a GenAI Engineer with strong hands-on experience in building end-to-end AI applications. The role requires integrating data from multiple systems, extracting and parsing information from documents and images, interacting with structured databases, storing parsed content in a vector database, and implementing robust retrieval pipelines. The engineer must also ensure solution quality through evaluation frameworks, ground-truth validation, and defined performance metrics., Strong experience in AI/ML engineering, with hands-on exposure to Generative AI use cases.

Experience in building RAG applications in enterprise environments.

Strong knowledge of document parsing, OCR, and image-based data extraction.

Experience with LLM orchestration frameworks and prompt design.

Experience with vector databases and semantic search.

Very Strong programming skills in Python.

Experience working with SQL/NoSQL databases and enterprise data integration patterns.

Understanding of evaluation frameworks for GenAI systems using benchmark datasets and ground-truth-based validation.

Experience in building scalable APIs/services and production-grade AI workflows.

Preferred Skills

Experience with Azure-based AI stack.

Experience with high-volume document processing.

Familiarity with enterprise architecture, security, and compliance controls.

Exposure to monitoring, model evaluation, and AI observability tools.

Preferred Profile

Able to independently build and deploy GenAI applications from ingestion to retrieval and evaluation.

Strong problem-solving skills with a practical implementation mindset.

Comfortable working across data engineering, AI engineering, and application integration.