Gen AI Developer

Walk- in drive on 31st Oct at TCS Hyderabad office (Invite basis):::
Minimum 4 to 10 Years of experience in using Python.
Minimum 4 years experience in Gen AI.
Strong proficiency in Python and experience with relevant libraries and frameworks (e.g., pandas, LangChain, LangGraph).
Strong experience working with API calls (FAST API, Rest API)
Proficiency with LLM integrations
At least one project is live in production
Ability to work efficiently within a fast-paced, deadline-driven environment, prioritize tasks, manage time effectively, and communicate challenges or issues proactively
Good to have experience working using MCP servers
Responsibilities::
Development & Implementation
Implement Python modules for data ingestion, prompt orchestration, and basic tool-agent workflows.
Build REST APIs/microservices (FastAPI/Flask) to connect LLMs with enterprise data sources.
Assist in developing Retrieval-Augmented Generation (RAG) components: ingestion, embeddings, chunking, and retrieval.
Configure and integrate LLMs (OpenAI, Gemini, Claude, Llama, Mistral) via APIs or SDKs.
Write unit/integration tests; contribute to robust error handling and logging.
Support feature deployment and troubleshoot issues in staging/production.
2. Data & Model Ops
Prepare datasets, metadata, and embeddings using vector databases (FAISS, Chroma, Pinecone, Weaviate).
Participate in evaluating model outputs; assist with prompt optimization and context management.
Help maintain experiment tracking and model/version metadata using MLOps tools (e.g., MLflow).
Contribute to metrics dashboards (latency, token usage, cost, accuracy).
3. Cloud Integration
Assist in deploying workloads to one hyperscaler (GCP, Azure, or AWS) under guidance:
GCP: Vertex AI, Cloud Run basics
Development & Implementation
Implement Python modules for data ingestion, prompt orchestration, and basic tool-agent workflows.
Build REST APIs/microservices (FastAPI/Flask) to connect LLMs with enterprise data sources.
Assist in developing Retrieval-Augmented Generation (RAG) components: ingestion, embeddings, chunking, and retrieval.
Configure and integrate LLMs (OpenAI, Gemini, Claude, Llama, Mistral) via APIs or SDKs.
Write unit/integration tests; contribute to robust error handling and logging.
Support feature deployment and troubleshoot issues in staging/production.
2. Data & Model Ops
Prepare datasets, metadata, and embeddings using vector databases (FAISS, Chroma, Pinecone, Weaviate).
Participate in evaluating model outputs; assist with prompt optimization and context management.
Help maintain experiment tracking and model/version metadata using MLOps tools (e.g., MLflow).
Contribute to metrics dashboards (latency, token usage, cost, accuracy).
3. Cloud Integration
Assist in deploying workloads to one hyperscaler (GCP, Azure, or AWS) under guidance:
GCP: Vertex AI, Cloud Run basics
Azure: Azure OpenAI, Cognitive Search fundamentals
AWS: Bedrock basics, Lambda/API Gateway wiring
Follow IAM/security best practices and cost-awareness guardrails.
4. Collaboration & Quality
Work in an agile squad with senior engineers, architects, and product owners.
Participate in code reviews and adhere to coding standards, documentation, and secure development practices.
Support demo builds, POCs, and reusable component libraries.
Communicate clearly on progress, blockers, and risks.
5. Continuous Learning & Innovation
Stay current with GenAI frameworks (LangChain, LangGraph, Google ADK), embeddings, and evaluation methods.
Experiment with PEFT/LoRA, prompt engineering techniques, and basic fine-tuning workflows.
Contribute to internal playbooks, examples, and knowledge-sharing sessions.

Work arrangement
No