Ai Ml Engineer

Orcapod Consulting Services; Ernakulam, Pune District, Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Orcapod Consulting Services seeks a Python AI/ML & Generative AI Engineer to design, develop, and deploy scalable AI/ML and GenAI solutions for enterprise applications across multiple locations in India.

The candidate will craft production-grade AI applications, integrate LLMs with enterprise data, and build intelligent agents and workflows, leveraging Python, ML/DL, RAG, and cloud platforms. Strong emphasis on security, governance, and performance.

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, ML, Data Science, or a related discipline
• 5+ years of hands-on Python development experience
• 2+ years of hands-on experience in AI/ML or Generative AI projects
• Strong understanding of ML algorithms, model evaluation, and data engineering concepts
• Strong hands-on experience developing production-grade AI/GenAI applications
• Strong problem-solving and communication skills

Responsibilities

• Design, develop, and deploy AI/ML and Generative AI solutions for enterprise applications
• Develop scalable AI applications using Python
• Build and optimize ML models for prediction, classification, recommendation, and automation use cases
• Develop GenAI applications using LLMs, RAG, vector databases, and AI agents
• Design and implement AI agents, agentic workflows, planning and multi-agent systems
• Develop RAG-based solutions using enterprise knowledge repositories and context-retrieval mechanisms
• Implement prompt engineering, context engineering, structured outputs, and AI guardrails
• Build scalable APIs and microservices using FastAPI or Flask
• Integrate LLMs through APIs such as OpenAI, Azure OpenAI, Anthropic, Bedrock, Gemini, or equivalent platforms
• Implement vector search and semantic retrieval using Pinecone, FAISS, ChromaDB, Weaviate
• Fine-tune, evaluate, and optimize LLMs and foundation models
• Implement MLOps/LLMOps practices for model deployment, monitoring, and lifecycle management
• Evaluate model performance, grounding quality, hallucination rates, and overall AI solution accuracy
• Collaborate with backend, platform, and business teams to convert AI use cases into production-ready solutions
• Ensure AI solutions meet security, governance, scalability, and performance requirements

Skills

Python

Machine Learning

Deep Learning

Generative AI

LLMs

RAG

AI Agents

LangChain

Vector Databases

FastAPI/Flask

Cloud platforms

Education

Bachelor's or Master's in CS/AI

Tools

LangGraph

Kubernetes

Docker

AWS/Azure/GCP AI services

Job description

Position: Python AI/ML & Generative AI Engineer

Experience: 5+ Years

Location-Bangalore,Kochi,Hyderabad,Pune,Chennai

Job Summary

We are looking for a highly skilled Python AI/ML & Generative AI Engineer to design, develop, and deploy scalable AI/ML and Generative AI solutions for enterprise applications.

The ideal candidate should have strong hands-on expertise in Python, Machine Learning, Deep Learning, LLMs, Generative AI, RAG, AI Agents, and cloud-based AI platforms. The candidate will be responsible for building production-ready AI applications, developing intelligent agents and workflows, integrating LLMs with enterprise data, and implementing scalable AI solutions.

Key Responsibilities

• Design, develop, and deploy AI/ML and Generative AI solutions for enterprise applications.
• Develop scalable AI applications using Python.
• Build and optimize ML models for prediction, classification, recommendation, and automation use cases.
• Develop GenAI applications using LLMs, RAG, vector databases, and AI agents.
• Design and implement AI agents, agentic workflows, planning and multi-agent systems.
• Develop RAG-based solutions using enterprise knowledge repositories and context-retrieval mechanisms.
• Implement prompt engineering, context engineering, structured outputs, and AI guardrails.
• Build scalable APIs and microservices using FastAPI or Flask.
• Integrate LLMs through APIs such as OpenAI, Azure OpenAI, Anthropic, Bedrock, Gemini, or equivalent platforms.
• Implement vector search and semantic retrieval using Pinecone, FAISS, ChromaDB, Weaviate, or similar technologies.
• Fine-tune, evaluate, and optimize LLMs and foundation models.
• Implement MLOps/LLMOps practices for model deployment, monitoring, and lifecycle management.
• Evaluate model performance, grounding quality, hallucination rates, and overall AI solution accuracy.
• Collaborate with backend, platform, and business teams to convert AI use cases into production-ready solutions.
• Ensure AI solutions meet security, governance, scalability, and performance requirements.

Mandatory Skills

• Python Strong hands-on experience
• Machine Learning: Scikit-Learn, XGBoost, LightGBM
• Deep Learning: TensorFlow / PyTorch
• Generative AI & LLMs
• Prompt Engineering
• RAG / Retrieval-Augmented Generation
• LangChain / LangGraph or equivalent agent-orchestration frameworks
• AI Agents / Agentic AI
• Vector Databases: Pinecone / FAISS / ChromaDB / Weaviate
• LLM APIs: OpenAI / Azure OpenAI / Anthropic / Bedrock / equivalent
• FastAPI / Flask
• SQL and NoSQL databases
• Experience with AWS / Azure / GCP AI services

Good-to-Have Skills

• MLOps / LLMOps
• Kubernetes and Docker
• Apache Spark / Databricks
• Knowledge Graphs
• NLP / Computer Vision
• Data Engineering Pipelines
• AI evaluation and observability frameworks
• AI Guardrails
• GitHub Copilot
• CI/CD and DevOps practices

Required Qualifications

• Bachelor's or Master's degree in Computer Science, AI, ML, Data Science, or a related discipline.
• 5+ years of hands-on Python development experience.
• 2+ years of hands-on experience in AI/ML or Generative AI projects.
• Strong understanding of ML algorithms, model evaluation, and data engineering concepts.
• Strong hands-on experience developing production-grade AI/GenAI applications.
• Strong problem-solving and communication skills.

Preferred Experience

• Enterprise AI assistants, copilots, chatbots, or GenAI applications.
• RAG-based solutions using enterprise knowledge repositories.
• Autonomous, tool-using, planning, and multi-agent systems.
• AI agent development using LangGraph/LangChain or equivalent frameworks.
• Deployment of AI/ML models using cloud platforms and MLOps tools.
• Agile/Scrum development environments.