{"id":2770155,"url":"https://alion.io/job/cognine-machine-learning-engineer-gcp","title":"Machine Learning Engineer (GCP)","company":{"id":4442648,"name":"Cognine","domain":"cognine.com","url":"https://alion.io/company/cognine","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":48000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-06-25T10:33:58Z","employer_posted_date":"2026-06-25","last_verified_at":"2026-10-18T04:59:26Z","board_verified":true,"closed_at":null,"days_open":114,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":114},"description":"Job Summary\nWe are looking for an experienced Machine Learning Engineer / MLOps Engineer to design, deploy, and maintain scalable machine learning solutions on Google Cloud Platform (GCP). The ideal candidate will have strong expertise in production ML systems, CI/CD automation, Vertex AI, and model lifecycle management while collaborating closely with cross-functional teams to operationalize machine learning models.\nKey Responsibilities:\nDesign, build, and maintain training and inference pipelines for storm outage prediction workflows.\nImplement CI/CD, orchestration, and automation for machine learning workflows using Vertex AI and related GCP services.\nDeploy machine learning models into production environments and manage model lifecycle processes, including versioning and rollout support.\nSet up and maintain baseline monitoring for model drift, performance, reliability, and alerting.\nCreate scalable, production-ready ML workflows and supporting technical documentation.\nCollaborate with data scientists, engineers, and project stakeholders to operationalize models and align deployment architecture with project needs.\nSupport troubleshooting, performance tuning, and continuous improvement of ML platform components.\nContribute to engineering best practices across code quality, release processes, and environment stability.\n\nRequired Qualifications:\n5-10 years of experience in machine learning engineering, MLOps, or related production ML engineering roles.\nStrong proficiency in Python and experience developing scalable data and ML workflows.\nHands-on experience with Google Cloud Platform, including Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, or comparable services.\nExperience with GitHub Actions or similar CI/CD tooling.\nDemonstrated experience building CI/CD pipelines and automating ML model deployment and orchestration.\nExperience with model monitoring, observability, and production support for machine learning systems.\nStrong understanding of software engineering best practices, including version control, testing, and documentation.\nAbility to work effectively across distributed teams and communicate clearly with technical and non-technical stakeholders.\n\nPreferred Qualifications:\nExperience supporting forecasting, outage prediction, or other data-intensive operational use cases.\nFamiliarity with utility, energy, weather, or geospatial data domains.\nExposure to model registry, retraining automation, and ML lifecycle governance practices.\nPrior experience working in offshore or globally distributed delivery models.","description_format":"text","description_chars":2579,"description_truncated":false,"requirements":{"experience_years_min":5,"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":[],"lifecycle":[{"event":"open","at":"2026-10-17T06:11:03Z"}],"visa":[],"liveness":{"score":10,"band":"cold","label":"Long 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