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Senior Artificial Intelligence Engineer

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Remote

Senior Artificial Intelligence Engineer

Siro Clinpharm

Senior Artificial Intelligence Engineer

Skills

Google Cloud PlatformDeep LearningDockerPythonMachine LearningAIAWSKubernetesNLP / Natural Language ProcessingApache AirflowAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Remote

Published on:

19 August 2026

What they need


JD : Job Title: Senior AI Engineer (Agentic AI & Enterprise AI Solutions)

Experience

  • Total Experience: 10+ years
  • Relevant AI/ML Experience: 5-6+ years
  • Location: Remote

Role Overview

We are seeking a Senior AI Engineer with strong expertise in modern AI paradigms (Agentic AI, AI Agents, MCP) to lead the design, development, and scaling of enterprise-grade AI solutions.

The ideal candidate will be a hands-on practitioner capable of rapidly developing POCs, translating business problems into AI-driven solutions, and defining scalable architecture and deployment roadmaps.

Key Skills

  • End-to-end AI/ML solution development and deployment
  • Rapid POC development (fast experimentation and validation)
  • Strong problem-to-solution translation for business use cases
  • AI architecture design and scalability planning
  • Performance optimization (latency, scalability, efficiency)
  • Cross-functional stakeholder collaboration
  • MLOps implementation and lifecycle management
  • Technical leadership and mentoring

Key Technologies

  • Machine Learning & Deep Learning frameworks
  • LLMs & Generative AI (RAG, prompt engineering, fine-tuning)
  • Agentic AI & AI Agents (multi-agent systems, orchestration)
  • MCP (Model Context Protocol) & tool integration frameworks
  • NLP and Conversational AI systems
  • Computer Vision solutions
  • AI frameworks: LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel
  • Programming: Python, API development
  • Cloud platforms: Azure / AWS / GCP
  • Data & pipeline tools: Spark, Airflow
  • Containerization: Docker, Kubernetes
  • MLOps tools (MLflow, CI/CD pipelines, monitoring & observability)

Modern AI Stack:

  • Agentic AI frameworks (LangGraph, AutoGen, CrewAI, etc.)
  • AI Agents & Multi-agent orchestration
  • MCP-based integrations and tool usage patterns
  • LLM frameworks (LangChain, Semantic Kernel, etc.)

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