{"id":3244127,"url":"https://alion.io/job/gccx-ai-mlops-support-engineer","title":"AI & MLOps Support Engineer","company":{"id":4442763,"name":"GCCX","domain":"gccxglobal.com","url":"https://alion.io/company/gccxglobal","size_band":null,"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":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AIOps","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"GCP","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Jenkins","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"SonarQube","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"TensorFlow","optional":false},{"name":"Vertex AI","optional":false},{"name":"Feature Store","optional":true},{"name":"Kubeflow","optional":true},{"name":"Machine Learning","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2026-10-25T18:35:30Z","employer_posted_date":null,"last_verified_at":"2026-10-25T18:35:30Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Responsibilities: \nDeploy, monitor, and manage ML/AI models on Vertex AI, including training pipelines, model endpoints, and batch prediction workflows.\nProvide MLOps support, including troubleshooting model performance, debugging ML pipelines, and resolving production incidents.\nImplement AIOps practices, including monitoring, alerting, and automated remediation for AI/ML systems using GCP tools.\nCollaborate with Data Scientists to operationalise ML models, optimise performance, and ensure reliability.\nManage CI/CD pipelines for ML workflows using tools like Cloud Build, GitHub Actions, or Jenkins.\nMonitor and optimise GCP resources such as Big Query, Cloud Storage, Dataflow, Dataproc, Cloud Functions, Cloud Run, and Pub/Sub for AI/ML workloads.\nEnsure compliance with security, governance, and data privacy best practices in ML workflows.\nBuild dashboards and observability solutions using Google Cloud Monitoring/Logging, Data Plex, and third-party tools.\nTroubleshoot and optimise ML pipelines, including data preprocessing, feature engineering, and model retraining.\n\nRequirements: \n2-4 years of experience in MLOps, AI operations, or ML engineering on cloud platforms, preferably GCP.\nHands-on experience with Vertex AI for model deployment, training pipelines, and endpoint management.\n2+ years of experience in code management and reliability using GitHub, pytest, and SonarQube.\n1+ years of experience with Tableau.\nStrong knowledge of MLOps practices, including CI/CD, versioning, automated testing, and model monitoring.\nProficiency in Python, SQL, and ML frameworks like TensorFlow or PyTorch.\nExperience with GCP services: Big Query, Cloud Storage, Dataflow, Dataproc, Cloud Functions, Cloud Run, Pub/Sub, Dataplex.\nFamiliarity with AI monitoring, alerting, and observability tools (Cloud Monitoring, Logging, Data plex).\nStrong problem-solving skills and ability to troubleshoot ML pipelines and AI system issues.\nUnderstanding of AIOps concepts, including automated anomaly detection, model drift detection, and incident response for AI systems.\n\nPreferred Qualifications: \nGCP certifications, such as Professional Machine Learning Engineer or Professional Data Engineer.\nExperience with feature stores, model registries, or MLflow.\nFamiliarity with Kubeflow, Airflow, or orchestration tools for ML pipelines.\nExperience in data privacy, governance, and compliance for ML systems.\nSkills\nGCP, Google Cloud, ML engineering, MLOps, Vertex AI","description_format":"text","description_chars":2464,"description_truncated":false,"requirements":{"experience_years_min":4,"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":"2026-10-25T18:35:30Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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