Associate Director, RDU IT - MLOps

Job summary

Leads the strategy, architecture, delivery, and governance of an enterprise AI platform, enabling teams to deploy production-grade machine learning, agentic AI, and generative AI securely and efficiently. Owns reusable platform services and MLOps/LLMOps pipelines while setting engineering standards, embedding responsible AI and regulatory controls, and improving reliability and unit economics across model and LLM workloads.

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.
• Technical leadership experience delivering platform capabilities.
• Hands-on experience with production models, data science operations, serverless architectures, Kubernetes, Docker, and model maintenance.
• Hands-on experience using Terraform and Terragrunt to provision and manage 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.
• Deep understanding of software development lifecycle and maintenance.
• Experience with orchestration tools such as Airflow, Flyte, or Kubeflow.
• Experience with vector databases, RAG patterns, LangChain or LlamaIndex-class frameworks, evaluation harnesses, and prompt and guardrail management.
• Strong Python programming skills and experience with TensorFlow, Keras, or PyTorch.
• Proficiency in MLOps practices, including model training, testing, deployment, and monitoring.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Understanding of software engineering best practices, agile methodologies, data structures, algorithms, and machine learning techniques.
• Strong communication, collaboration, independent working, and problem-solving skills.
• Fluency in English.
• Pharmaceutical industry experience and a related master's degree or ongoing advanced-degree study are desirable.

Responsibilities

• Own the AI/ML platform vision, target architecture, roadmap, and engineering delivery.
• Build reusable frameworks, reference architectures, APIs, templates, and self-service platform services.
• Ensure platform security, resilience, scalability, and cost-effectiveness using infrastructure as code, monitoring, incident management, capacity planning, and disaster recovery.
• Set engineering standards and reusable delivery patterns, including secure CI/CD, automated testing, code quality, vulnerability management, observability, and production support.
• Lead solution design and decisions across security, integration, data, performance, scalability, interoperability, and cost.
• Lead engineering teams and communicate delivery progress, risks, decisions, and outcomes to stakeholders and senior management.
• Own MLOps and LLMOps architecture, development, and governance, integrating workflows securely with enterprise data, applications, and IT systems.
• Manage platform cost transparency, including cost per inference, GPU utilization, token economics, and showback or chargeback.
• Embed security, privacy, data governance, model risk management, responsible AI, validation, documentation, human oversight, traceability, and auditability.
• Promote responsible AI adoption and AI-enabled capabilities across IT operations.

Skills

Degrees

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Industry

Relocation