Senior ML Ops Engineer - Nas Neuron

Job Description The Senior MLOps Engineer is responsible for operationalising and maintaining machine learning, Generative AI, and agent-based solutions in production. The role focuses on ensuring that AI services are deployed through controlled pipelines, monitored continuously, tested systematically, and operated with the reliability, security, scalability, and cost discipline required for enterprise use. Working under the Principal – ML Platform Engineer, the role provides the operational discipline required to move AI solutions from experimentation into stable production service. This includes managing CI/CD, versioning, observability, model and agent monitoring, automated testing, incident response, release controls, and performance reporting. The Senior MLOps Engineer is expected to identify failures or degradation early, strengthen production runbooks, and continuously improve the effectiveness and resilience of deployed AI systems. Alongside this specialism, every engineer in the Strategy and Data team is expected to own use cases end-to-end, taking them from problem definition through to a deployed, adopted solution. Responsibilities and Duties • Design, build, and maintain production-grade MLOps pipelines covering deployment, release management, monitoring, automated testing, versioning, rollback, retraining support, and decommissioning. • Establish and operate CI/CD pipelines for machine learning, Generative AI, and agent-based workloads using appropriate enterprise tooling such as Azure DevOps, GitHub, GitLab CI, Jenkins, or equivalent platforms. • Package, containerise, deploy, and maintain models, APIs, AI agents, RAG workflows, and inference services across controlled development, test, staging, and production environments. • Implement automated testing for model services, prompts, agent workflows, APIs, data dependencies, deployment packages, and release gates to reduce production risk and improve service reliability. • Monitor model, agent, and service performance, including latency, throughput, error rates, hallucination or response-quality signals, drift indicators, data quality issues, dependency failures, and user-impacting incidents. • Develop observability dashboards, alerts, logs, traces, and health checks that provide early warning of failures, degradation, cost anomalies, security risks, or operational instability. • Maintain model and agent versioning, artifact repositories, configuration records, environment definitions, dependency controls, and release documentation to support reproducibility and auditability. • Support production incident management for AI services, including triage, root-cause analysis, remediation, escalation, post-incident review, and preventive improvement actions. • Prepare clear performance and operational reporting covering service health, reliability, release quality, incident trends, utilisation, cost, and improvement actions for technical and management stakeholders. • Monitor cloud and AI service consumption, identify cost drivers, recommend optimisation opportunities, and support capacity planning for production AI workloads. • Work with AI engineers, data engineers, software engineers, IT, and security teams to ensure deployments follow approved architecture, security, privacy, responsible AI, and governance standards. • Support integration between Snowflake, Microsoft Azure, data pipelines, vector stores, model services, agent orchestration layers, monitoring tools, and downstream enterprise applications. • Maintain operating procedures, runbooks, release checklists, escalation paths, environment standards, and reusable deployment templates to strengthen the organisation’s production AI capability. • Continuously improve the stability, scalability, security, and effectiveness of production AI systems, ensuring that deployed solutions remain aligned with business priorities and operational needs. • Take assigned use cases through each stage, from framing the problem with business stakeholders and preparing data, to building, evaluating and deploying the solution and tracking its adoption and value, working within the team's architecture and engineering standards. Knowledge, Skills and Experience • 8+ years of relevant experience across MLOps, DevOps, platform engineering, data engineering, software engineering, or machine learning engineering, with direct exposure to production AI, analytics, or data services. • Strong practical experience with Python, SQL, Git/GitHub, MLflow, Azure AI Foundry, Snowflake/Snowpark, Docker, Kubernetes, FastAPI, Airflow, Azure Data Factory, Azure Data Lake, Azure Container Apps, Microsoft Azure, Entra ID, Key Vault, Private Link, Terraform, REST APIs, Linux environments, cloud deployment patterns, and production support practices. • Demonstrated experience operating CI/CD pipelines, release controls, branching strategies, pull requests, automated testing, artifact management, environment promotion, and rollback processes. • Hands-on experience deploying and operating containerised AI services using Kubernetes, Azure Kubernetes Service, Azure App Service, Azure Functions, or equivalent platforms. • Practical knowledge of Microsoft Azure services relevant to AI and MLOps, including Azure Machine Learning, Azure AI Foundry, Azure OpenAI, Azure Monitor, Application Insights, Log Analytics, Key Vault, Storage, and networking controls. • Experience with Snowflake or equivalent cloud data platforms, including integration with model training, inference services, feature access, orchestration layers, and downstream applications. • Familiarity with MLOps and LLMOps practices such as MLflow, model registries, experiment tracking, model artifact management, prompt evaluation, agent monitoring, vector stores, embeddings, and RAG workflow operations. • Strong understanding of observability, including metrics, logs, traces, alerts, service-level indicators, drift monitoring, model degradation, inference lat