{"id":3947774,"url":"https://alion.io/job/iris-software-group-ml-ops-lead","title":"ML Ops Lead","company":{"id":51452,"name":"IRIS Software Group","domain":"iris.co.uk","url":"https://alion.io/company/iris-software-group","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":86,"open_postings":13,"ghost_share":0,"stale_share":0.462,"repost_share":0.154,"time_to_fill_p50_days":27,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-11-08T22:36:40Z","employer_posted_date":null,"last_verified_at":"2026-11-08T22:36:40Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Job Description:\n\nMandatory Skills:\nMachine Learning (ML)\nCI/CD (for ML pipelines)\nModel Deployment & Serving\nModel Lifecycle Management\nModel Registry & Experiment Tracking\nPython\n\nAdditional Skills:\nAdvanced GenAI & Agentic Framework Concepts\nNLP (Natural Language Processing)\nPyTorch\nDatabricks\n\nKey Responsibilities:\nDefine and drive enterprise MLOps strategy aligned with organizational AI, machine learning, and operational objectives.\nEstablish MLOps governance frameworks, engineering standards, and deployment best practices across teams.\nLead the design of enterprise-scale CI/CD architectures supporting machine learning lifecycle automation.\nDefine enterprise standards for model deployment, serving, and operational management.\nEstablish governance standards for model registry, version management, model traceability, and lifecycle control.\nDefine experiment tracking standards supporting reproducibility, auditability, and model governance requirements.\nDrive automation initiatives focused on accelerating model deployment, operational stability, and release consistency.\nDefine enterprise standards for model monitoring, prediction quality evaluation, data drift detection, concept drift analysis, model health monitoring, and retraining governance to ensure long-term production model effectiveness.\nLead architecture reviews and ensure MLOps solutions meet scalability, reliability, maintainability, and governance objectives.\nGuide teams on MLOps architecture, deployment frameworks, model lifecycle management, and operational best practices.\nIdentify deployment risks, governance gaps, operational bottlenecks, and lifecycle management challenges while defining mitigation strategies.\nCollaborate with various teams and leadership stakeholders to align MLOps initiatives with organizational objectives.\nDrive continuous improvement initiatives focused on MLOps maturity, automation effectiveness, operational excellence, and governance.\n\nBehavioral Competencies:\nDemonstrates leadership and accountability in driving MLOps excellence across programs and initiatives.\nCollaborates effectively with various teams and business stakeholders to ensure smooth delivery.\nPromotes a culture of automation, reliability, governance, and continuous improvement.\nApplies strategic thinking to address deployment challenges, operational risks, and business priorities.\nDemonstrates strong decision-making while balancing scalability, reliability, governance, and delivery objectives.\nCommunicates effectively regarding MLOps strategy, risks, dependencies, and operational outcomes.\nMaintains a proactive approach toward engineering governance, operational maturity, and solution excellence.\nEncourages innovation and continuous learning within MLOps, machine learning operations, and deployment engineering practices.\nDrives collaboration and alignment across teams to achieve organizational objectives.\nDemonstrates a customer-centric mindset by delivering reliable, scalable, and high-quality ML operational solutions.\nSkills\nCI / CD, Cicd, MLOps, 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