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What you’ll bring

All qualifications

Core experience

Strong Python programming with FastAPI and Flask
Experience with autonomous agents and async workflows
Solid understanding of ML, Deep Learning, NLP, and GenAI concepts
Knowledge of AI governance, monitoring, and security
Hands-on experience with LLMs, embeddings, and prompt engineering
Experience designing agentic systems with MCP / A2A

Qualification wording

Strong Python programming with FastAPI and Flask

Experience with autonomous agents and async workflows

Solid understanding of ML, Deep Learning, NLP, and GenAI concepts

Knowledge of AI governance, monitoring, and security

Hands-on experience with LLMs, embeddings, and prompt engineering

Experience designing agentic systems with MCP / A2A

Tools in this posting

JavaScript
Python
AWS
Azure
Docker
Google Cloud (GCP)
Kubernetes
MLflow
SageMaker
Fastapi
Huggingface
Flask

Source

- Expertise in RAG architectures and vector databases (FAISS, Pinecone, Weaviate, Chroma, Milvus) - Working knowledge of front-end technologies: HTML, CSS, JavaScript, Stremlit; React preferred - API design and integration (REST / WebSockets)

Required Skills - Strong Python programming with FastAPI and Flask - Solid understanding of ML, Deep Learning, NLP, and GenAI concepts

- API design and integration (REST / WebSockets) - Strong deployment & MLOps experience: Azure / AWS / GCP, Docker, Kubernetes, CI/CD, MLflow / Azure ML / SageMaker / Vertex AI Good to Have

- Build and deploy LLM-based GenAI applications end-to-end - Work with multiple LLMs (OpenAI/Azure OpenAI, Gemini, Claude, LLaMA, Mistral) - Design and implement agentic and multi-agent systems using MCP and A2A

- Use modern GenAI frameworks (LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Google ADK, MS CoPilot) - Build AI backend services using FastAPI and Flask - Develop and integrate chat UIs, copilots, and dashboards with AI APIs

Good to Have - Fine-tuning open-source LLMs (LoRA, PEFT, Hugging Face) - Experience with autonomous agents and async workflows

Job description

View original posting ↗

AI/ML Engineer1

Key Responsibilities

Build and deploy LLM-based GenAI applications end-to-end
Work with multiple LLMs (OpenAI/Azure OpenAI, Gemini, Claude, LLaMA, Mistral)
Design and implement agentic and multi-agent systems using MCP and A2A
Develop RAG pipelines and document intelligence solutions
Use modern GenAI frameworks (LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Google ADK, MS CoPilot)
Build AI backend services using FastAPI and Flask
Develop and integrate chat UIs, copilots, and dashboards with AI APIs
Productionize, deploy, and monitor AI systems on cloud platforms
Ensure performance, security, scalability, and Responsible AI compliance

Required Skills

Strong Python programming with FastAPI and Flask
Solid understanding of ML, Deep Learning, NLP, and GenAI concepts
Hands-on experience with LLMs, embeddings, and prompt engineering
Experience designing agentic systems with MCP / A2A
Expertise in RAG architectures and vector databases (FAISS, Pinecone, Weaviate, Chroma, Milvus)
Working knowledge of front-end technologies: HTML, CSS, JavaScript, Stremlit; React preferred
API design and integration (REST / WebSockets)
Strong deployment & MLOps experience: Azure / AWS / GCP, Docker, Kubernetes, CI/CD, MLflow / Azure ML / SageMaker / Vertex AI

Good to Have

Fine-tuning open-source LLMs (LoRA, PEFT, Hugging Face)
Experience with autonomous agents and async workflows
Exposure to RPA + Agentic AI integration
Knowledge of AI governance, monitoring, and security

Your next step

Have your CV and examples of relevant work ready.
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Source & posting history

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Source notes

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Pay

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Location & working pattern

Pune, Maharashtra

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Open source at jadeglobal.wd5.myworkdayjobs.com ↗Read the full description ↓

Status in our records: Active
First seen by us: Jul 1, 2026
Recorded sightings: 157
Last seen by us: Oct 8, 2026

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