Python Automation

Role : Python Automation

Experience : 6 to 10 years

Location : Chennai, Kolkata, Hyderabad, bangalore,Pune, Delhi

Keywords : Python development and Generative AI expertise to build and scale AI-first platforms and applications. The role focuses on developing and operationalizing GenAI solutions on Google Cloud Vertex AI, while also integrating capabilities across AWS and OpenAI ecosystems.

The ideal candidate will have deep experience in LLM-based application development, RAG pipelines, and GenAI frameworks such as LangChain and LangGraph, combined with strong platform engineering and cloud architecture skills.

Role descriptions / Expectations from the Role :

Vertex AI & GenAI Platform Development (Core Focus)

• Design, build, and manage GenAI platforms leveraging Google Vertex AI
• Develop reusable platform components and services for:
• LLM orchestration
• Prompt management
• RAG pipelines
• Enable scalable deployment of LLM-powered applications across teams
• Build internal frameworks, APIs, and SDKs for AI use cases

GenAI Application Development

• Develop AI applications using:
• LangChain, LangGraph
• RAG (Retrieval-Augmented Generation) pipelines
• Build use cases such as:
• AI copilots
• Conversational agents
• Document intelligence systems
• Integrate models via:
• Vertex AI (Gemini models)
• OpenAI APIs / Azure OpenAI
• AWS Bedrock

Python Development (Core Requirement)

• Build scalable backend services using Python
• Use frameworks such as:
• FastAPI / Flask
• Develop orchestration layers for multi-step GenAI workflows (LangGraph)
• Write clean, modular, and reusable code for AI systems

RAG Pipelines & Data Engineering

• Design and implement end-to-end RAG pipelines
• Data ingestion, chunking, embeddings, indexing, retrieval
• Work with vector databases:
• Pinecone, FAISS, Chroma, Weaviate
• Optimize retrieval quality and response accuracy
• Handle structured & unstructured enterprise data

Cloud & Multi-Platform Engineering (GCP + AWS/OpenAI)

• Architect solutions primarily on GCP Vertex AI, with integration to:
• AWS (Bedrock, Lambda, S3)
• OpenAI ecosystem
• Use services like:
• Vertex AI Workbench, Model Garden, Pipelines
• Cloud Run, BigQuery, Pub/Sub
• Design multi-cloud GenAI architectures when required

LLMOps / MLOps

• Build and maintain LLMOps pipelines:
• Prompt versioning
• Model evaluation
• Monitoring & logging
• Track key metrics:
• Latency, token usage, cost, response quality
• Implement guardrails for safe and responsible AI

DevOps & Platform Reliability

• Build CI/CD pipelines for GenAI applications and services
• Use containerization (Docker) and orchestration (GKE/Kubernetes)
• Ensure platform:
• Scalability
• Reliability
• Cost efficiency
• Implement observability (logs, metrics, tracing)