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Ai Ml Engineer

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Onsite

Ai Ml Engineer

Orcapod Consulting Services

Ai Ml Engineer

Skills

Google Cloud PlatformSQL QueriesData EngineeringDeep LearningDockerFlaskPythonData ScienceMicroservicesPyTorchTensorFlowDatabricksMachine LearningAIAWSGitHubKubernetesNLP / Natural Language ProcessingFastAPIAzure

1 month ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

1 October 2026

What they need


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.

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