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Salary
$21k – $32k per year (gross)
Location
In office (India)
Seniority
Middle · 4+ years exp

First seen by Alion on Sep 16, 2026.

Overview
Company
Impact
Profile match
Ensuring your business meets the highest industry standards. Achieve and maintain critical certifications that drive operational excellence, security and compliance.

AI Solution Engineer - No Moonlighting candidates.

Full Time Position with MNC

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.

Tech Stack & Skills:

- 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.

Skills

Machine Learning, Python, Artificial Intelligence, RAG, Azure, Copilot, Flask, FastAPI, Generative AI, Cloud

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