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.