Associate Director, RDU IT - MLOps

Job summary

Leads the strategy, architecture, delivery, and governance of an enterprise AI platform that enables teams to deploy production-grade machine learning, agentic AI, and generative AI securely and cost-effectively. Owns reusable platform services, MLOps and LLMOps pipelines, engineering standards, responsible AI controls, and cost management, using technologies such as Kubernetes, Terraform, Python, CI/CD frameworks, and cloud platforms.

Qualifications

• At least 8 years of experience in software, cloud, DevOps, or AI/ML engineering.
• At least 4 years deploying and maintaining machine learning models in production.
• Experience leading technical teams and delivering platform capabilities.
• Hands-on experience with production models, serverless architectures, Kubernetes, Docker, and model maintenance.
• Hands-on experience using Terraform and Terragrunt to provision secure, repeatable cloud environments.
• Familiarity with API-based application architecture and API frameworks.
• Experience with CI/CD orchestration frameworks such as GitHub Actions, Jenkins, or Bitbucket Pipelines.
• Extensive experience with orchestration tools such as Airflow, Flyte, or Kubeflow.
• Experience with vector databases, RAG patterns, LangChain- or LlamaIndex-class frameworks, evaluation harnesses, prompts, and guardrails.
• Experience with MLOps tools for experiment tracking, model registries, and feature stores, such as MLflow, SageMaker, or Azure services.
• Strong Python programming skills and experience with TensorFlow, Keras, or PyTorch.
• Proficiency in MLOps practices, including model training, testing, deployment, and monitoring.
• Experience with AWS, Azure, or Google Cloud Platform.
• Strong understanding of software development lifecycle, software engineering practices, data structures, algorithms, machine learning, and agile methodologies.
• Strong communication and collaboration skills in cross-functional teams; able to work independently and solve problems.
• Fluent English.
• Pharmaceutical industry experience and advanced-degree study are desirable.

Responsibilities

• Own the AI/ML platform vision, architecture, roadmap, and engineering delivery.
• Build reusable frameworks, APIs, templates, reference architectures, and self-service platform services.
• Ensure platforms are secure, resilient, scalable, observable, and cost-effective.
• Set engineering standards and reusable delivery patterns, including secure CI/CD, testing, vulnerability management, and release controls.
• Lead solution design and technical decisions across security, integration, data, performance, scalability, interoperability, and cost.
• Own the architecture, development, and governance of MLOps and LLMOps pipelines.
• Manage platform cost models, track inference costs, GPU utilization, and token economics, and operate showback or chargeback mechanisms.
• Embed privacy, data governance, model risk management, responsible AI, validation, documentation, human oversight, and auditability.
• Lead engineering teams and communicate delivery progress, risks, decisions, and outcomes to stakeholders.
• Promote AI adoption and AI-enabled capabilities across IT operations.

Skills

Degrees

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Industry

Relocation