Applied AI Research

Role Description

As the Director of Applied AI Research in the Platform Engineering team, you will define and lead the applied research agenda for enterprise AI/GenAI. You will identify and rigorously evaluate emerging models, techniques, and tools that can improve business value, engineering productivity, risk management, and platform capabilities. Your scope will include foundation models, retrieval-augmented generation, agentic architectures, multimodal AI, fine-tuning, evaluation, and other rapidly evolving approaches.

This is a hands-on research leadership role focused on converting technical uncertainty into evidence-based enterprise decisions. You will establish disciplined experimentation and benchmarking practices, build reference prototypes, partner with architecture, capability development, AI-Ops, security, risk, and business teams, and build a research function that advances innovation while maintaining responsible-AI, privacy, security, and regulatory expectations.

Role Objectives

• Set the applied AI research agenda in alignment with enterprise strategy, platform priorities, business opportunities, and risk considerations.
• Evaluate foundation models, frameworks, and tools across quality, safety, latency, cost, scalability, privacy, and operational readiness.
• Lead experiments and benchmarks for retrieval-augmented generation, agentic systems, multimodal AI, fine-tuning, synthetic data, small language models, and other emerging techniques.
• Build reference prototypes and reusable research assets that demonstrate feasibility and create a clear transition path into architecture, capability development, and platform engineering roadmaps.
• Establish repeatable evaluation methodologies, benchmark datasets, scoring rubrics, experiment tracking, and documentation standards that support reproducible and auditable conclusions.
• Advance research on responsible AI, model safety, bias, explainability, privacy, security, and governance controls appropriate for regulated enterprise use.
• Maintain a structured view of the external AI ecosystem, including cloud providers, research organizations, vendors, and open-source communities, and recommend technologies for adoption or further study.
• Translate research findings into clear executive guidance, technical standards, architecture decisions, investment recommendations, and platform roadmap inputs.
• Build, mentor, and lead an applied AI research team while strengthening experimentation, technical writing, and knowledge-sharing practices across the engineering organization.

Qualifications and Skills

• Bachelor's degree in Computer Science, Machine Learning, Data Science, Statistics, Engineering, or a related field; a master's degree or Ph.D. is strongly preferred.
• 8+ years of hands-on experience in applied machine learning, AI research, or advanced AI engineering, including 3+ years in a research leadership, principal scientist, architect, or technical lead capacity.
• Strong expertise in experimental design, statistical analysis, benchmarking, ablation studies, and reproducible evaluation of complex AI systems.
• Advanced Python skills and hands-on expertise with machine learning libraries and model ecosystems such as PyTorch, TensorFlow, Hugging Face Transformers, and commercial foundation-model APIs.
• Deep knowledge of foundation models, retrieval-augmented generation, agentic systems, multimodal AI, fine-tuning, embeddings, vector databases, and model evaluation.
• Demonstrated experience evaluating AI systems for quality, safety, robustness, latency, cost, and operational suitability using quantitative and qualitative methods.
• Hands-on experience with cloud and data platforms such as Azure, Databricks, MLflow, vector search, model serving, and experiment tracking.
• Proven ability to move research into production by partnering with architects, platform engineers, capability teams, and AI-Ops practitioners.
• Strong understanding of responsible AI, model risk, privacy, cybersecurity, explainability, and governance expectations in a regulated environment.
• Excellent technical writing, executive communication, and team leadership skills, with the ability to explain complex research findings to technical and non-technical audiences.