{"id":1266213,"url":"https://alion.io/job/quickhyre-ai-engineer","title":"AI Engineer","company":{"id":3808061,"name":"QuickHyre","domain":"quickhyre.ai","url":"https://alion.io/company/quickhyre","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":3400000,"max":9000000,"currency":"INR","period":"year","gross":true,"usd_annual":94338},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Function Calling","optional":false},{"name":"Go","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Rust","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-09-07T09:34:58Z","employer_posted_date":null,"last_verified_at":"2026-09-07T09:34:58Z","board_verified":false,"closed_at":null,"days_open":35,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":35},"description":"Job Summary : \n\nIn this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required - your domain knowledge is what matters.\n\nAs an expert you will be creating Reinforcement Learning Environments which test an AI model's ability to solve complex software engineering problems using Model Context Protocol (MCP) tools. Tasks may involve fixing bugs, implementing features, refactoring code, or optimizing performance while requiring agents to discover and reason over information from real MCP servers. 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