Senior AI Engineer — AI Platform & LLMOps

About the job

One of our clients is seeking a hands-on engineer to take predictive, generative and agentic AI from development into governed production.

Responsibilities

• Work with AI enablement and IT teams to translate business needs into prediction, classification, generative AI, retrieval or automation solutions.
• Design, train, evaluate and deploy ML and generative AI using Azure AI Foundry, Copilot Studio, Vertex AI and Snowflake Cortex.
• Connect enterprise agents to trusted data through secure APIs and MCP, with embeddings and retrieval over proprietary information.
• Run disciplined experiments to select and evaluate models for accuracy, performance and business suitability.
• Move solutions into production and verify release readiness against platform operations standards.
• Own model versions, registration, retraining, promotion and retirement throughout the lifecycle.
• Define drift and degradation thresholds and retraining criteria; tune or remediate solutions while coordinating retraining schedules and registry operations.
• Document expected behavior, dependencies and failure modes so operations teams can prepare runbooks, on-call support and handovers.
• Build reusable training, evaluation and deployment pipelines across environments with Azure DevOps or GitHub Actions.
• Automate tests and infrastructure or configuration changes for repeatable, auditable releases of models, prompts and agents.
• Use Azure ML, MLflow and model or feature registries for experiments, tracking and deployment.
• Automate offline and online evaluations and regression checks for predictive and generative applications.
• Make pipelines observable and supportable before handing them to operations.
• Establish reference architectures, engineering standards and reusable AI frameworks.
• Write development, evaluation, deployment, monitoring and support playbooks that make ownership clear.
• Mentor colleagues in machine learning, AI operations and agent engineering.
• Apply lessons from delivered projects to shared practices and the operating model.
• Implement guardrails for appropriate data and model use, safe operation and compliance.
• Protect ML, agent and retrieval systems against prompt injection and unauthorized data access.
• Provide logging, observability, auditability and explainability for compliance and troubleshooting.
• Track quality, performance, reliability and cost, and improve results against agreed measures.

Required Qualifications

• Eight to ten years in ML, AI or software engineering, including at least three years of production ML and recent generative AI or agent work.
• A bachelor’s or advanced degree in computer science, data science, engineering or a related field.
• Strong Python and SQL skills, with scikit-learn, PyTorch or TensorFlow experience.
• Practical experience with one or more of Azure AI Foundry, Copilot Studio, Claude, Cursor, Vertex AI or Snowflake Cortex.
• Experience with multi-environment MLOps, CI/CD, Azure DevOps or GitHub Actions, Azure ML, MLflow and model lifecycle management.
• Experience with agent orchestration, RAG, vector databases, embeddings, prompts and MCP or API integrations.
• Ownership of AI systems from development through monitoring, drift management, retraining and production support.
• Knowledge of evaluation, testing, observability, responsible AI, security and governance.
• Experience in a multicloud environment with Azure as the primary platform, Google Cloud as a secondary platform, and Snowflake or Microsoft Fabric for enterprise data.
• Ability to mentor colleagues and promote practical learning and innovation.
• Strong collaboration and communication with technical and business stakeholders.
• An analytical approach focused on measurable business results.