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We are seeking a hands-on AI/ML Engineer specializing in MLOps and Site Reliability Engineering (SRE) to build, operate, and continuously improve production-grade machine learning systems. In 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 reusable MLOps platform 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-level objectives (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: secrets management, 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 on MLOps and reliability standards. 

Required Qualifications 

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field with 5+ years of relevant experience; OR a Master's/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 to operate effectively in cross-functional teams. 

Preferred Qualifications 

• Experience with ML platform tools such as MLflow, 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., Evidently, whylogs, custom statistical checks). 

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

• Contributions to open-source projects, publications, or demonstrated technical 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 by team needs; candidates should be comfortable learning and adapting to new tools.