AI Engineer

MGT Briefing:

Introduction: The MGT & LearningMate/Straive Partnership
MGT and LearningMate/Straive have established a strategic global partnership designed to deliver world-class advisory, technology, and operational solutions for public sector and education clients. This collaboration leverages MGTs deep expertise in management consulting and technology services with LearningMate/Straives global leadership in educational technology and talent solutions.
Through this partnership, LearningMate/Straive serves as a key sourcing and onboarding partner for MGT, enabling access to a diverse pool of global talent and specialized resources. New roles are sourced and hired in close coordination between both organizations, with LearningMate/Straive managing recruitment, onboarding, and initial training.
The partnership is built on a foundation of shared valuescommitment to equity, innovation, and social impact. Together, MGT and LearningMate/Straive are able to scale services, accelerate project delivery, and offer compelling career opportunities for professionals passionate about making a difference in education and public service.
As a member of the team, you will benefit from robust onboarding, ongoing training, and the support of both organizations leadership. You’ll play a vital role in delivering impactful solutions that help communities thrive, while advancing your own career in a dynamic, global environment.

MGT.US

AI Engineer Role, Responsibilities & Skills

Experience Required: 4+ Years

Experience

• 4+ years of hands-on experience in AI/ML engineering, applied GenAI, or software engineering building AI-enabled systems.

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 (AWS/Azure) 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.
• Cloud: AWS (S3, IAM, CloudWatch) with SageMaker for training/inference and deployment patterns.
• Good to have: Azure ecosystem familiarity (data/AI services) and hybrid cloud architectures.
• Good to have: ETL concepts and tools; familiarity with AWS Glue and data pipeline patterns.