3 to 10 Years of experience using Python

. • Solid proficiency in Python and experience with relevant libraries and frameworks (e.g., pandas, LangChain, LangGraph).
Strong experience working with API calls (QUICK API, Rest API)
Proficiency with LLM integrations
At least one project is live in production
Ability to work efficiently within a rapid-paced, deadline-driven environment, prioritize tasks, manage time effectively, and communicate challenges or issues proactively
Positive to have experience working using MCP servers. 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.
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).
Cloud Integration

• Assist in deploying workloads to one hyperscaler (GCP, Azure, or AWS) under guidance: o GCP: Vertex AI, Cloud Run basics

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