AIML Engineer MLOpsSRE

We are seeking a hands-on AI/ML Engineer specializing inMLOpsand Site Reliability Engineering (SRE) to buildoperate and continuously improve production-grade machine learning this role you will partner with data scientists data engineers and software teams to standardize the ML lifecycle improve reliability and performance and enable rapid safe delivery of models and AI services at scale.

Key Responsibilities

• Production ML Platform & Tooling

• Design and implement reusableMLOpsplatform capabilities for training deployment and monitoring of ML/LLM systems.

• Build standardized pipelines for data validation feature generation training evaluation model packaging and release.

• Own model registry artifact storage and metadata lineage to ensure reproducibility and auditability.

• Deployment Engineering & Release Safety

• Deploy models and AI services using containers and orchestration (e.g. Kubernetes) with robust rollout strategies (blue/green canary A/B).

• Create CI/CD workflows for ML code and pipelines including automated tests quality gates and approval controls.

• Harden inference services for low latency and high throughput using caching batching autoscaling and efficient model serving patterns.

• Reliability Observability & Incident Response (SRE)

• Define and track service-level indicators (SLIs) and service-levelobjectives(SLOs) for ML services pipelines and data dependencies.

• Implement end-to-end observability: structured logging metrics tracing dashboards and alerting for both infrastructure and model behavior.

• Lead incident response and post-incident reviews; drive systemic fixes through runbooks automation and reliability engineering practices.

• Model & Data Monitoring

• Implement monitoring for model quality and data health: drift bias performance degradation and data pipeline anomalies.

• Build automated feedback loops to trigger investigations retraining workflows and safe rollback when quality thresholds are breached.

• Security Compliance & Governance

• Integrate security best practices:secretsmanagement least-privilege access (RBAC) network controls and vulnerability scanning.

• Support compliance and governance requirements for model usage data access retention and responsible AI practices.

• Collaboration & Enablement

• Partner with data science and engineering teams to translate business requirements into reliable scalable ML solutions.

• Create developer-friendly documentation templates and internal best practices; mentor teams onMLOpsand reliability standards.

Required Qualifications

• Bachelors degree in Computer Science Engineering Data Science or a related field with 5 years of relevant experience; OR a Masters/PhD with 3 years of relevant experience.

• Proven experience deploying and operating ML models or AI services in production environments.

• Strong programming skills in Python and experience with common ML libraries and frameworks (e.g.PyTorch TensorFlow scikit-learn).

• Hands-on DevOps/SRE experience: CI/CD infrastructure as code containerization and operational excellence.

• Experience with cloud platforms and managed services (Azure AWS or GCP) and building scalable secure systems.

• Experience with Kubernetes and modern model serving patterns (REST/gRPC async workers batch/stream inference).

• Strong understanding of monitoring and observability (metrics logs traces) and incident management processes.

• Ability to communicate clearly with both technical and non-technical stakeholders and tooperateeffectively in cross-functional teams.

Preferred Qualifications

• Experience with ML platform tools such asMLflow Kubeflow Airflow SageMaker Vertex AI or Azure Machine Learning.

• Experience with feature stores data quality frameworks and dataset/versioning tools (e.g. Feast Great Expectations DVC).

• Experience with distributed systems performance tuning (autoscaling queueing caching load shedding).

• Experience implementing model monitoring for drift bias and quality (e.g. Evidentlywhylogs custom statistical checks).

• Knowledge of security and compliance patterns for enterprise AI (data classification encryption audit logging).

• Contributions to open-source projects publications ordemonstratedtechnical leadership through talks/blogs.

What Success Looks Like (First 6-12 Months)

• Standardized CI/CD and deployment patterns for ML services that reduce time-to-production while improving safety and reliability.

• Clear SLOs dashboards and alerts for critical AI services with measurable improvements in uptime latency and incident response.

• Automated monitoring and quality checks that detect drift and data issues early with repeatable remediation workflows.

• Improved reproducibility and governance through consistent artifact tracking lineage and documentation.

Note: Technology choices may vary byteamneeds; candidates should be comfortable learning and adapting to new tools.