The company is looking to build Generative AI (GenAI) capabilities and enhance its AI-powered applications and data engineering workflows to deliver accessible, high-quality, and sustainable healthcare.
Requirements
- Experience with retrieval-augmented generation (RAG), fine-tuning pre-trained LLMs, AI model evaluation, data pipeline automation, and optimizing cloud-based AI deployments.
- Experience with building and optimizing scalable data pipelines for AI/ML workflows using Pandas, PySpark, and Dask, integrating data sources such as Kafka, AWS S3, Azure Data Lake, and Snowflake.
- Experience in assessing AI models using model scoring, fine tuning embeddings and enhance similarity search for retrieval augmented applications
- Experience on enhancing AI model inference efficiency by implementing vector retrieval using FAISS, Pinecone, or ChromaDB, and optimize API latency with tuning techniques (temperature, top-k sampling, max tokens settings).
- Design and develop scalable RESTful APIs for AI models and data services, ensuring integration with internal and external systems while securing API endpoints using OAuth, JWT authetication, and API rate limiting.
- Implement AI-powered logging, observability, and monitoring to track data pipelines, model drift, and inference accuracy, ensuring compliance with AI governance and security best practices.
- Experience with GenAI Frameworks & Tools: OpenAI API, Hugging Face Transformers, TensorFlow, LangChain, LlamaIndex, CrewAI.
Responsibilities
- Develop AI-driven applications, microservices, and automation.
- Build and maintain Python based AI services using lang Chain and CrewAI.
- Integrate and optimize OpenAI APIs (GPT models, Embeddings, Function Calling), hugging Face, LangChain, and implement Retrieval-Augmented Generation (RAG) techniques to enhance AI-powered document retrieval and classification, agentic AI workflows
- Deploy AI-powered applications using AWS Lambda, Kubernetes, Docker, CI/CD pipelines
- Building and optimizing scalable data pipelines for AI/ML workflows using Pandas, PySpark, and Dask, integrating data sources such as Kafka, AWS S3, Azure Data Lake, and Snowflake.
- Assessing AI models using model scoring, fine tuning embeddings and enhance similarity search for retrieval augmented applications
- Enhancing AI model inference efficiency by implementing vector retrieval using FAISS, Pinecone, or ChromaDB, and optimize API latency with tuning techniques (temperature, top-k sampling, max tokens settings).
Other
- Work collaboratively across Technology, Product, AI/ML, and DevOps teams to align AI-driven enhancements with business goals.
- Build strong relationships with AI engineers, data scientists, and cloud architects to optimize LLM-based applications.
- Ensure AI compliance with security, ethical AI policies, and privacy standards (HIPAA, GDPR, SOC2, AI governance best practices).
- Mentor junior engineers on AI model integration, API development, and scalable data engineering best practices, and conduct knowledge-sharing sessions.
- Hands-on experience with Agile development, SDLC, CI/CD pipelines, and AI model deployment lifecycles.
