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Principal Member of Technical Staff- Machine Learning & AI
ConfidentialChennai, India9 days ago
Job description. Design, build, and optimize 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. . Enhance 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). . Develop scalable RESTful APIs for AI models and data services, ensuring integration with internal and external systems while securing API endpoints using OAuth, JWT, and API Key Authentication. . 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. . Work with AI / ML, Data Engineering, and DevOps teams to optimize AI model deployments, data pipelines, and real-time / batch processing for AI-driven solutions. . Engage in Agile ceremonies, backlog refinement, and collaborative problem-solving to scale AI-powered workflows in areas like fraud detection, claims processing, and intelligent automation. . Collaborate with Product, UX, and Compliance teams to align AI-powered features with user needs, security policies, and regulatory frameworks (HIPAA, GDPR, SOC2). . Ensure seamless integration of structured and unstructured data sources (SQL, NoSQL, vector databases) to improve AI model accuracy and retrieval efficiency. . 12-18 years of experience in software engineering or AI / ML development, preferably in AI-driven solutions. . Hands-on experience with Agile development, SDLC, CI / CD pipelines, and AI model deployment lifecycles. . Bachelor's Degree or equivalent in Computer Science, Engineering, Data Science, or a related field. . Proficiency in full-stack development with expertise in Python (preferred for AI), Java . Experience with structured & unstructured data : . SQL (PostgreSQL, MySQL, SQL Server) . NoSQL (OpenSearch, Redis, Elasticsearch) . Vector Databases (FAISS, Pinecone, ChromaDB) . AWS : Lambda, SageMaker, ECS, S3 . Azure : Azure OpenAI, ML Studio . GenAI Frameworks & Tools : OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, AutoGPT, CrewAI. . Experience in LLM deployment, retrieval-augmented generation (RAG), and AI search optimization. . Proficiency in AI model evaluation (BLEU, ROUGE, BERT Score, cosine similarity) and responsible AI deployment. . Strong problem-solving skills, AI ethics awareness, and the ability to collaborate across AI, DevOps, and data engineering teams. . Curiosity and eagerness to explore new AI models, tools, and best practices for scalable GenAI adoption.
Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.
This position requires expertise in designing, developing, debugging, and maintaining AI-powered applications and data engineering workflows for both local and cloud environments. The role involves working on large-scale projects, optimizing AI / ML pipelines, and ensuring scalable data infrastructure.
As a PMTS, you will be responsible for integrating Generative AI (GenAI) capabilities, building data pipelines for AI model training, and deploying scalable AI-powered microservices. You will collaborate with AI / ML, Data Engineering, DevOps, and Product teams to deliver impactful solutions that enhance our products and services.
Additionally, it would be desirable if the candidate has experience in retrieval-augmented generation (RAG), fine-tuning pre-trained LLMs, AI model evaluation, data pipeline automation, and optimizing cloud-based AI deployments.
Responsibilities
- . AI-Powered Software Development & API Integration
- . Develop AI-driven applications, microservices, and automation workflows using FastAPI, Flask, or Django, ensuring cloud-native deployment and performance optimization.
- . Integrate OpenAI APIs (GPT models, Embeddings, Function Calling) and Retrieval-Augmented Generation (RAG) techniques to enhance AI-powered document retrieval, classification, and decision-making.
Data Engineering & AI Model Performance Optimization
Microservices, APIs & Security
AI & Data Engineering Collaboration
Cross-Functional Coordination and Communication
Mentorship & Knowledge Sharing
Mentor junior engineers on AI model integration, API development, and scalable data engineering best practices, and conduct knowledge-sharing sessions.
Education & Experience Required
Cloud & AI Infrastructure :
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Member Of Technical Staff • Chennai, India
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