{"id":1285298,"url":"https://alion.io/job/upcruit-ai-production-support-engineer","title":"AI Production Support Engineer","company":{"id":3800780,"name":"Upcruit","domain":"upcruit.ai","url":"https://alion.io/company/upcruit","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":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India","Gurgaon, India","India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":51000,"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":"Amazon CloudWatch","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"DynamoDB","optional":false},{"name":"GDPR","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Grafana","optional":false},{"name":"IAM","optional":false},{"name":"Incident Management","optional":false},{"name":"ITIL","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"ServiceNow","optional":false},{"name":"Splunk","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-08-17T07:18:32Z","employer_posted_date":null,"last_verified_at":"2026-08-17T07:18:32Z","board_verified":false,"closed_at":null,"days_open":58,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":58},"description":"We are looking for an AI Production Support Engineer to support and operate AI/ML solutions within a regulated banking environment. The role focuses on ensuring high availability, resilience, compliance, and risk management of AI systems that support critical banking services.\n\nKey Responsibilities :\n\n- Provide L2/L3 production support for AI/ML models and data pipelines used in banking systems\n\n- Monitor model performance, drift, data quality, and operational health of AI services\n\n- Ensure stability and uptime of AI platforms supporting customer-facing and regulatory workloads\n\n- Perform incident management, root cause analysis (RCA), and problem management in line with ITIL practices\n\n- Collaborate with Data Science, Engineering, Risk, and Compliance teams\n\n- Support secure deployment, release, and rollback of models in production\n\n- Implement monitoring, alerting, and audit logging to meet regulatory and audit requirements\n\n- Ensure adherence to data privacy, governance, and financial regulatory standards (e.g., GDPR, model risk frameworks)\n\n- Support disaster recovery (DR) and business continuity (BCP) plans for AI workloads\n\n- Identify opportunities for automation, operational efficiency, and cost optimisation\n\nRequired Skills & Experience :\n\n- Experience in production support / SRE / platform engineering, preferably in banking or financial services\n\n- Strong understanding of AI/ML lifecycle and model operations (MLOps)\n\n- Experience with cloud platforms (Azure preferred in banking), including secure workloads\n\n- Proficiency in Python and scripting for debugging and automation\n\n- Hands-on experience with Docker, Kubernetes, and microservices architectures\n\n- Familiarity with MLOps tools (MLflow, Azure ML, SageMaker, etc.)\n\n- Experience with monitoring & observability tools (CloudWatch, Splunk, Grafana, Prometheus)\n\n- Knowledge of data pipelines, APIs, batch and real-time processing systems\n\n- Experience with incident management tools (e.g., ServiceNow)\n\nEssential Knowledge (Including Tooling) : \n\n- Understanding of model risk management (MRM) and audit expectations\n\n- Awareness of data governance, lineage, and controls\n\n- Familiarity with security standards and identity access management (IAM)\n\n- Cloud & AI Platforms (AWS) : AWS SageMaker, EC2, EKS (Elastic Kubernetes Service), Lambda, S3, CloudWatch\n\n- MLOps & Model Management : SageMaker Pipelines, MLflow, model registry and deployment frameworks\n\n- Containerisation & Orchestration : Docker, Kubernetes (EKS)\n\n- Monitoring & Observability : AWS CloudWatch, CloudTrail, Prometheus, Grafana, OpenTelemetry\n\n- CI/CD & DevOps : AWS CodePipeline, CodeBuild, CodeDeploy, Jenkins, GitHub Actions\n\n- Data & Integration : AWS Glue, Kinesis, EventBridge, REST APIs, SQL/NoSQL (RDS, DynamoDB)\n\n- Security & Identity : IAM, AWS KMS, Secrets Manager, VPC security (subnets, NACLs, security groups)\n\n- Resilience & Backup : AWS Backup, cross-region replication, DR strategies (multi-AZ / multi-region)\nSkills\nArtificial Intelligence, Production Support, Machine 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