Thrive IT Systems
GenAI Engineer
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About the Company
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.
About the Role
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.
Responsibilities
- 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
- Follow enterprise standards for security, governance, observability, and performance
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Qualifications
Strong experience in AIML engineering with hands-on exposure to Generative AI use cases.
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Required Skills
- 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
- 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
Mandatory Skills
- GenAI - LLMOps, Python
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