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AI Solution Engineer

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Remote

AI Solution Engineer

Rnrcertification

AI Solution Engineer

Skills

FlaskPythonMachine LearningAIAWSFastAPIAzure

1 month ago

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Important information


Contract type:

Permanent

Salary:

₹2,000,000 – ₹3,000,000 / year

Location:

India

Work mode:

Remote

Published on:

16 September 2026

What they need


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.

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