GenAI Adoption - Platform

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