{"id":1227229,"url":"https://alion.io/job/hexacorp-engineering-manager","title":"Engineering Manager","company":{"id":3800378,"name":"HexaCorp","domain":"hexacorp.com","url":"https://alion.io/company/hexacorp","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Leadership","role_family":"Leadership","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":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":39000,"max_usd":97000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":987},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false}],"status":"live","first_seen_at":"2026-09-25T07:27:08Z","employer_posted_date":null,"last_verified_at":"2026-09-25T07:27:08Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Purpose & Scope :\n\nThe ML Ops Engineering Manager is responsible for leading the delivery and operational excellence of ML Ops capability - the infrastructure, pipelines, and practices that take machine learning and AI models from development into reliable, governed production use.\n\nThis role manages a team of ML Ops engineers and partners closely with Data Science, Data Engineering, Platform, and Governance teams to deliver scalable, secure, and well-monitored model deployment and operations across growing AI portfolio.\n\nThe ML Ops Engineering Manager focuses on execution, engineering rigor, team leadership, and cross-functional coordination, while model strategy and prioritization remain with Data Science and AI leadership.\n\nWhat you will be doing (responsibilities) :\n\nML Ops Delivery Leadership :\n\n- Lead end-to-end delivery of CI/CD pipelines, model registry practices, and deployment infrastructure for ML and AI use cases.\n\n- Drive predictable execution of the ML Ops roadmap in partnership with Data Science and Platform leadership.\n\n- Establish and enforce engineering standards for model packaging, testing, deployment, and rollback.\n\n- Proactively manage delivery risks, technical dependencies, and production incidents across deployed models.\n\nPlatform & Architecture Alignment :\n\n- Ensure ML Ops solutions align with enterprise data and AI platform standards and architecture patterns.\n\n- Partner with platform and architecture teams to design scalable, cost-effective serving and training infrastructure.\n\n- Guide teams on appropriate use of shared compute, environments, and model infrastructure.\n\nMonitoring, Governance & Trust :\n\n- Embed model monitoring, drift detection, and performance alerting into pipelines as standard practice.\n\n- Ensure model versioning, lineage, and documentation requirements are met to support auditability.\n\n- Partner with data governance, security, and compliance teams to ensure responsible and compliant AI deployment.\n\nEngineering Excellence & Operational Readiness : \n\n- Drive CI/CD maturity for ML pipelines, including automated testing, staged rollouts, and controlled promotions.\n\n- Ensure deployed models are operationally ready with monitoring, alerting, and clear incident ownership.\n\n- Continuously improve reliability, latency, and cost-efficiency of training and inference workloads.\n\nStakeholder & Cross-Functional Collaboration :\n\n- Partner with Data Science and AI leadership to translate model roadmaps into executable engineering deliverables.\n\n- Collaborate with Data Engineering to ensure consistent, high-quality data feeds into ML pipelines.\n\n- Communicate delivery status, risks, and trade-offs clearly to stakeholders and leadership.\n\nPeople Leadership & Team Development :\n\n- Manage, mentor, and develop a team of ML Ops engineers across experience levels.\n\n- Set clear expectations around quality, delivery discipline, and operational ownership.\n\n- Foster a culture of automation, documentation, and continuous improvement.\n\nWhat you bring (Qualifications) :\n\nRequired :\n\n- 8 - 10 years of experience in MLOps, ML engineering, DevOps, or platform engineering, including team or delivery leadership.\n\n- Strong hands-on background in CI/CD, containerization, and orchestration for ML workloads.\n\n- Experience operating model registries, monitoring tooling, and ML pipelines in enterprise environments.\n\n- Working knowledge of cloud platforms (Azure preferred) and Databricks-based ecosystems.\n\n- Strong stakeholder management skills across data science, engineering, platform, and governance teams.\n\nPreferred :\n\n- Experience supporting AI/ML programs in retail, consumer goods, or other data-intensive industries.\n\n- Familiarity with LLM/GenAI deployment patterns and evaluation practices.\n\n- Exposure to enterprise data governance and AI risk/compliance frameworks.\n\nSuccess Measures :\n\n- Predictable, governed delivery of ML Ops capabilities with reduced rework.\n\n- Improved model deployment reliability, observability, and incident response.\n\n- Increased reuse of standardized deployment patterns across model teams.\n\n- High stakeholder confidence in the reliability and execution of the ML Ops function.\nSkills\nEngineering Management, MLOps, Machine Learning, CI/CD Pipeline, Monitoring Tools, Cloud, Azure","description_format":"text","description_chars":4292,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","AI Consulting & Integration","Cloud Consulting & Migration"],"lifecycle":[{"event":"open","at":"2026-09-25T13:06:44Z"}],"liveness":{"score":95,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.946,"p_room":1,"age_days":0,"expected_fill_days":35,"reasons":["seen:0","urgency","win:early"],"computed_at":"2026-09-26T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/hexacorp-engineering-manager","json_url":"https://alion.io/job/hexacorp-engineering-manager.json","meta":{"generated_at":"2026-09-27T03:24:36Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3213,"day_limit":5000,"remaining_today":1787,"minute_limit":60,"resets_at":"2026-09-28T00:00:00Z"}}}