Designation: Senior Generative AI Engineer
Experience: 5-8 Years
Function: Engineering / Artificial Intelligence & Machine Learning

JOB SUMMARY

We are looking for a Senior Generative AI Engineer with 5-8 years of software development experience and strong expertise in building enterprise-grade AI/ML and Generative AI solutions using Large Language Models (LLMs), NLP and cloud-based AI platforms.

The role requires hands-on expertise in Python, GenAI frameworks, RAG, scalable API development, vector search and document intelligence, with a strong focus on developing solutions that are traceable, explainable and auditable.

Experience in the US Healthcare domain and healthcare AI/document processing will be an added advantage.

KEY RESPONSIBILITIES

• Design and develop production-grade Generative AI applications using GPT, Claude, Gemini and/or open-source LLMs.
• Build AI/NLP pipelines for document extraction, classification, summarization and structured JSON generation.
• Design and implement Retrieval-Augmented Generation (RAG) and vector search solutions.
• Develop scalable REST APIs and microservices for AI-driven workflows.
• Optimize prompt engineering, context management, model responses and hallucination-reduction techniques.
• Build and integrate OCR and document intelligence pipelines for large-scale document processing.
• Ensure AI solutions are traceable, explainable and auditable.
• Implement evaluation, monitoring and performance optimization frameworks for AI systems.
• Collaborate with engineering, product and cross-functional teams to deliver scalable, production-ready AI solutions.

REQUIRED SKILLS & EXPERIENCE

• 8+ years of software development experience with strong hands-on Python expertise.
• Strong experience in Generative AI, LLMs and NLP.
• Hands-on experience with OpenAI / Azure OpenAI.
• Experience with GenAI frameworks such as LangChain, LlamaIndex and/or Semantic Kernel.
• Strong understanding of RAG, embeddings and vector databases.
• Experience in prompt engineering, context optimization and hallucination reduction.
• Experience with JSON schema-based structured data extraction.
• Strong understanding of REST APIs and microservices architecture.
• Experience with cloud platforms, preferably Microsoft Azure.
• Familiarity with Docker, Kubernetes, Git and CI/CD pipelines.
• Working knowledge of SQL and NoSQL databases.
• Experience working within Agile and software release/development cycles.
• Strong understanding of building traceable, explainable and auditable AI solutions.

GOOD TO HAVE – US HEALTHCARE

Experience in the US Healthcare domain, particularly in one or more of the following:

• ICD-10 / HCC / Risk Adjustment
• EMR/EHR systems
• Clinical NLP
• Healthcare document processing
• FHIR / HL7 standards
• OCR and document intelligence pipelines
• Healthcare-focused AI/GenAI solutions

PREFERRED QUALIFICATIONS

• Bachelor's or Master's degree in Computer Science, AI/ML, Data Science or a related field.
• Experience building and deploying enterprise-scale AI platforms and solutions.
• Strong analytical, problem-solving and communication skills.
• Ability to work effectively with technical and cross-functional stakeholders.

KEY TECHNICAL STACK

Python | LLMs | GPT | Claude | Gemini | Azure OpenAI | LangChain | LlamaIndex | Semantic Kernel | RAG | NLP | Prompt Engineering | Vector Databases | Embeddings | REST APIs | Microservices | OCR | Document Intelligence | JSON Extraction | Azure | Docker | Kubernetes | Git | CI/CD | SQL | NoSQL

• Design and develop production-grade Generative AI applications using GPT, Claude, Gemini and/or open-source LLMs.
• Build AI/NLP pipelines for document extraction, classification, summarization and structured JSON generation.
• Design and implement Retrieval-Augmented Generation (RAG) and vector search solutions.
• Develop scalable REST APIs and microservices for AI-driven workflows.
• Optimize prompt engineering, context management, model responses and hallucination-reduction techniques.
• Build and integrate OCR and document intelligence pipelines for large-scale document processing.
• Ensure AI solutions are traceable, explainable and auditable.
• Implement evaluation, monitoring and performance optimization frameworks for AI systems.
• Collaborate with engineering, product and cross-functional teams to deliver scalable, production-ready AI solutions.