AI/ML Engineer
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Before you apply
LocationPune, Maharashtra
Company size1,001–5,000 people
PostedJan 7, 2026 · 10mo ago
Verified live2d ago
At a glanceSummarised by Seekless from the posting.
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)
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
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, Streamlit; 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
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
Skills
Python
FastAPI
Flask
LLMs
OpenAI
Azure OpenAI
Gemini
Claude
LLaMA
Mistral
MCP
A2A
RAG
LangChain
LangGraph
LlamaIndex
CrewAI
AutoGen
Google ADK
FAISS
Pinecone
Weaviate
Chroma
Milvus
HTML
CSS
JavaScript
Streamlit
React
Azure
AWS
GCP
Docker
Kubernetes
CI/CD
MLflow
Azure ML
SageMaker
Vertex AI
LoRA
PEFT
Hugging Face
AI/ML Engineer2
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)
•
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