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).
- Robust 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.

📌 Gen AI Developer (Hyderabad)
🏢 Tata Consultancy Services
📍 Hyderabad