AI Solution Engineer – Agent Systems (Remote India) | LearningMate x MGT

About the Company: Global Leaders in Intelligent Public Sector Architecture

LearningMate (operating globally in alliance with Straive) is an international pioneer in digital education infrastructure, data engineering, and sustainable software platforms. For over two decades, LearningMate has partnered with Tier-1 universities, K-12 school districts, and global educational publishers to deliver highly accessible, resilient, and optimised technical ecosystems.

In a high-growth strategic partnership, LearningMate acts as the primary global talent and technology orchestration node for MGT, a premier U.S.-based management consulting and technology advisory firm serving state, local, and education government (SLED) agencies. With almost 1,200 specialised employees, MGT manages, secures, and modernises critical public networks, solves intricate human capital challenges, and advances equity as a performance imperative. Experiencing explosive organic growth alongside 13 strategic acquisitions since 2020, the MGT and LearningMate alliance integrates high-scale software development with deep public-sector workflows to deploy technical solutions that help communities across the United States thrive.

About the Role: Senior Software Engineer – Agent Systems (Azure)

Are you a seasoned software craftsman who bridges the gap between production-grade enterprise backends and modern artificial intelligence? LearningMate, in tight coordination with MGT, is seeking a pragmatic, system-minded AI Solution Engineer / Software Engineer (Agent Systems) to join our global engineering network via a fully remote setup in India (with our regional talent operations head office anchored in Chennai).

This is emphatically not an R&D experimentation or demo-building role. You will be joining an elite engineering group tasked with architecting and scaling a durable, enterprise-grade multi-agent AI ecosystem natively on Microsoft Azure. As a senior-track developer, you will think in broad systemic architectures rather than isolated sprint tickets. You will spend your days writing highly maintainable backend services, implementing real-world agent orchestration patterns, establishing robust memory systems, and building secure tool-calling APIs that tie LLMs directly into complex public sector workflows. The ideal candidate values strong engineering hygiene, understands how to make pragmatic architectural trade-offs for distributed scale, and comfortably moves production deployments forward under ambiguous requirements.

Key Responsibilities & AI System Engineering Workflows

• Agent Ecosystem Architecture: Design, build, test, and scale a production-ready autonomous agent framework running natively on Microsoft Azure infrastructure.

• Orchestration & Memory Design: Implement durable agent orchestration workflows, stateful memory patterns, complex reasoning loops, and deterministic tool-calling systems.

• Enterprise API Engineering: Build, secure, and maintain highly performant backend microservices and APIs to support secure data exchange across multi-agent layers.

• Enterprise Data Integration: Securely connect autonomous agent systems with disparate legacy public-sector databases, CRM frameworks, and internal cloud storage environments.

• Engineering Hygiene & CI/CD: Own absolute code quality, end-to-end integration testing, identity mapping, and observability metrics to transition agent networks seamlessly from prototype to production.

• Standardisation Leadership: Author internal engineering frameworks, prompt management guidelines, and tool-calling standards to reduce long-term structural complexity.

Candidate Prerequisites & Technical Matrix

We are looking for a software-first builder who values systemic reliability and maintainability over academic curiosity, and who designs systems with production observability built in from day one.

Minimum Required Qualifications:

• Educational Track: Bachelor’s degree from an accredited university in Computer Science, Software Engineering, Information Systems, or a closely related technical discipline.

• Experience Horizon: 3 to 8 years of total professional software engineering experience (with a minimum of 5+ years shipping high-scale production systems).

• Azure Infrastructure Core: Extensive hands-on background building cloud-native distributed architectures natively on Microsoft Azure (APIs, messaging queues, storage systems, identity management, and security protocols).

• Backend Languages: Absolute proficiency in at least one core backend language optimized for Azure development—specifically C#, Python, or TypeScript.

• DevOps Foundations: Deep familiarity with modern deployment operations, immutable infrastructure concepts, and automated CI/CD pipelines.

• Pragmatic Execution: Proven ability to make concrete architectural trade-offs, operate autonomously inside loose project definitions, and prioritize production readiness over temporary demos.

Highly Preferred Technical Assets (Nice to Have):

• Practical experience deploying tools inside Azure AI Studio / Azure AI Foundry, Semantic Kernel, or the Microsoft Agent Framework.

• Hands-on integration experience utilizing Copilot Studio, Microsoft SDKs, and the Microsoft Graph API ecosystem.

• Solid conceptual or production understanding of prompt management platforms, LLM evaluation, and agentic memory loops.

Key Technical Competencies

• Systems Thinking: The instinct to evaluate features based on their long-term architectural complexity and maintenance costs rather than isolated performance.

• Outcome Ownership: A strong builder mentality focused on driving projects through to deployment without waiting for perfectly structured technical specifications.

• AI Pragmatism: A realistic, engineering-focused view of current Large Language Model limitations, focusing heavily on safety, observability, and absolute reliability.