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    Job · 18055303

    REMOTEFULLTIME

    AI Solution Engineer

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    Remote · remote · Posted 35d ago

    Sourced fromNaukri
    ₹20.0L - ₹30.0L PA4–7 yrsfulltimeRemote

    About a minute. You'll make an account on the way through.

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    Section · 01

    About this role

    AI Solution Engineer--No Moonlighting candidates.


    Full Time Position with MNC

    AI Engineer Role, Responsibilities & Skills


    Role

    • Own AI requirements development and technical solutioning for AI RFPs/proposals and internal AI initiatives.
    • Build agent-driven workflows (Copilot Studio) and deploy AI/GenAI components on cloud platforms (Azure mandatory & AWS good to have) to production standards.

    Responsibilities

    • Conduct stakeholder discovery and translate business goals into AI use cases, requirements, and acceptance criteria.
    • Define data requirements: sources, access, quality, governance and retention.
    • Design GenAI/ML approaches (RAG, fine-tuning, agent workflows) with clear assumptions and tradeoffs.
    • Create evaluation criteria and validation plans (offline tests, human review, regression).
    • Break down AI RFP/RFI requirements into scope, risks, dependencies, and level-of-effort estimates.
    • Write proposal-ready technical narratives: architecture, methodology, implementation plan, and MLOps/LLMOps.
    • Build rapid demos/POCs to validate feasibility (retrieval, tool/function calling, integrations).
    • Develop and orchestrate agents in Microsoft Copilot Studio (connectors, actions, governance).
    • Implement and deploy solutions on AWS/Azure; leverage SageMaker and cloud-native services for scalable inference.
    • Collaborate with SMEs and delivery teams to create reusable assets (templates, prompts/modules) and smooth handoffs.

    Skills (Required + Good to Have)

    • Strong Python development; experience building APIs/services (e.g., FastAPI/Flask) and integrating enterprise systems.
    • GenAI systems: RAG pipelines, prompt/tool routing, grounding/guardrails, and evaluation frameworks.
    • Mandatory: Cloud Azure ecosystem familiarity (data/AI services) and hybrid cloud architectures.
    • Good to have: Cloud AWS (S3, IAM, CloudWatch) with SageMaker for training/inference and deployment patterns.
    • Good to have: ETL concepts and tools; familiarity with AWS Glue and data pipeline patterns.

    Preferred candidate profile



    Sourced from naukri

    Section · 02

    Skills

    Retrieval Augmented GenerationcopilotPythonMicrosoft AzureArtificial IntelligenceMachine Learning

    AI Solution Engineer · waiting for your command