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Sutherland Direct test for AI Platform Engineer freshers apply fast . This is a great opportunity for freshers and early-career professionals looking to kickstart their journey in a global IT services company. The position is based in India and eligible candidates are encouraged to apply online at the earliest. The detailed eligibility criteria and application process are given below. Also Check course section free
ABOUT COMPANY
Artificial Intelligence. Automation. Cloud Engineering. Advanced Analytics.
For Enterprises, these are key factors of success. For us, they’re our core expertise.
For Enterprises, these are key factors of success. For us, they’re our core expertise.
We work with global iconic brands. We bring them a unique value proposition through market-leading technologies and business process excellence. At the heart of it all is Digital Engineering – the foundation that powers rapid innovation and scalable business transformation.
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JOB DESCRIPTION
Extend and harden an AI-driven software development lifecycle platform built on Kiro IDE. The platform uses autonomous agents, MCP tool integrations (Rally, Playwright, Git, Confluence), steering files, and event-driven hooks to orchestrate requirements gathering, architecture design, code generation, test automation, and deployment gating — all AI-first.
build new agent capabilities, design RAG pipelines for context-aware code generation, create governance guardrails, and integrate with enterprise ALM/DevOps tools. This is not a testing role or a DevOps role — it’s building the platform that makes all SDLC phases AI-native.
Responsibilities:
- Design and build AI agents that orchestrate multi-step SDLC workflows (fetch → generate → execute → validate → sync)
- Build RAG pipelines that feed architecture docs, coding standards, and Rally artifacts as grounded context to LLMs
- Implement MCP servers and tool integrations (Rally, Playwright, Git, Confluence, Azure DevOps)
- Author steering files (persona, governance, lifecycle) that constrain agent behavior with formal rules
- Build self-healing and auto-remediation systems with bounded autonomy and human escalation
- Design state machines for artifact lifecycles (requirements → design → code → test → deploy)
- Create event-driven hooks for CI/CD integration, nightly runs, and release gating
- Implement property-based testing to formally verify system correctness
- Package and deploy agent infrastructure (Docker, pipelines, credential management)
Nice-to-Have
- MCP (Model Context Protocol) experience or similar tool-use protocols
- Playwright test automation
- Rally / Azure DevOps API integration
- Property-based testing (fast-check, QuickCheck)
- Salesforce application
- Experience with Kiro, Cursor, Windsurf, or similar AI-native IDEs
- Formal verification or correctness-by-construction approaches
Must-Have Skills
- AI Agent Systems — Built or extended multi-step autonomous agents with tool-use, guardrails, and human-in-the-loop patterns
- RAG Pipelines — Designed retrieval-augmented generation systems (embedding, chunking, vector DB, context injection)
- TypeScript / Node.js — Production-grade, 4+ years
- LLM Integration — Azure OpenAI or equivalent; prompt engineering, structured outputs, function calling
- Full-Stack Architecture — Can design end-to-end systems (API, queue, DB, frontend, infra)
- CI/CD & DevOps — Pipeline authoring, Docker, scheduled automation, release gates
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