Job descriptionJob Description Role : GenAI Adoption - Platform Experience : 6 to 10 years Location : Chennai, Kolkata, Hyderabad, bangalore,Pune, Delhi Skill set : Platform Engineer with strong Python and Generative AI expertise to design, build, and scale AI-first platforms and infrastructure Role descriptions / Expectations from the Role This role focuses on enabling GenAI application development at scale, building reusable frameworks, and integrating LLM capabilities across enterprise systems using AWS/GCP/OpenAI ecosystems. GenAI Platform Engineering (Core Focus) - Design and build scalable GenAI platforms and frameworks for enterprise use - Develop reusable components for: - Prompt orchestration - RAG pipelines - LLM integrations - Enable internal teams to rapidly build GenAI applications - Standardize GenAI usage across the organization (templates, SDKs, APIs) GenAI Application Enablement - Support development of AI-powered applications such as: - Chatbots and copilots - Document intelligence platforms - AI-driven workflow automation - Integrate with: - OpenAI / Azure OpenAI / Google Vertex AI - Implement: - Prompt engineering frameworks - LLM guardrails and evaluation layers Python Development (Core Skill) - Build backend services, libraries, and APIs using Python - Develop platform tooling using: - FastAPI / Flask - Create SDKs/microservices for GenAI feature reuse - Optimize system performance, scalability, and reliability Cloud Platform Engineering (AWS/GCP) - Architect and deploy platform services on: - AWS: Bedrock, Lambda, S3, SageMaker, EKS - GCP: Vertex AI, Cloud Run, BigQuery, GKE - Design multi-tenant, scalable AI platforms - Manage infrastructure as code (IaC) with Terraform or similar tools - Monitor usage, cost, and performance of GenAI workloads Data & AI Engineering - Build and maintain RAG pipelines with vector databases: - Pinecone, FAISS, Chroma, Weaviate - Manage embeddings, indexing, and retrieval systems - Handle structured and unstructured data pipelines - Ensure data security and governance in AI workflows DevOps, MLOps & LLMOps - Build CI/CD pipelines for platform services and AI models - Implement LLMOps practices: - Prompt versioning - Model lifecycle management - Evaluation pipelines - Set up observability: - Logging, tracing, monitoring for AI systems - Ensure system reliability, scaling, and failover strategies
