Applied AI Research

Sumitomo Mitsui Banking Corporation • Charlotte, NC

23h • Hybrid

About The Position

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

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

Requirements

• Bachelor's degree in Computer Science, Machine Learning, Data Science, Statistics, Engineering, or a related field.
• At least 8 years of hands-on experience in applied machine learning, AI research, or advanced AI engineering, including at least 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, including the ability to explain complex research findings to technical and non-technical audiences.

Nice To Haves

• A master's degree or Ph.D. in a relevant field is strongly preferred.

Responsibilities

• 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 establish a 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 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Director