GenAI Engineer

Role Summary

Seeking aGenAI Engineerwith 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.

Key Responsibilities

Design and buildGenAI/RAG-based applicationsusing data from multiple enterprise systems.

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

Develop pipelines toparse and extract data from documents and imagesusing OCR, document intelligence, and related tools.

Process and structure extracted content for downstream AI use cases.

Store parsed and chunked content in avector databaseand manage embeddings effectively.

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

Build workflows to interact withrelational and enterprise databasesfor querying and enrichment.

Ensure the application follows strongevaluation practices, including accuracy, groundedness, relevance, hallucination checks, and response quality againstground truth.

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

Follow enterprise standards forsecurity, governance, observability, and performance.

Required Skills and Experience

Strong experience inAI/ML engineering, with hands-on exposure toGenerative AIuse cases.

Experience in buildingRAG applicationsin enterprise environments.

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

Experience withLLM orchestration frameworksand prompt design.

Experience withvector databasesand semantic search.

Very Strong programming skills inPython.

Experience working withSQL/NoSQL databasesand enterprise data integration patterns.

Understanding ofevaluation frameworksfor GenAI systems using benchmark datasets and ground-truth-based validation.

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

Preferred Skills

Experience withAzure-based AI stack.

Experience withhigh-volume document processing.

Familiarity withenterprise architecture, security, and compliance controls.

Exposure tomonitoring, 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.