AI Solutions Engineer

AI Solutions Engineer

Role Description As a Staff AI/GenAI Engineer in the Platform Engineering team, you will play a pivotal role in designing, developing, and deploying AI platforms and solutions that drive business value. You will work closely with stakeholders in architecture, technology, data and business organizations. You will partner with Azure, Databricks, and other infrastructure providers to build and operate the AI/GenAI platform, ensuring robust, scalable, and secure architecture. To succeed in this role, you should be a fast learner who can quickly learn upcoming AI/GenAI technologies, and an accomplished coder capable of developing enterprise-scale applications. This is a unique opportunity to own the GenAI technology stack, ensuring industrial-grade performance, compliance, and efficiency in a high-stakes financial environment.

Role Objectives

• Define AI Platform architecture: Design and architect a scalable, secure, and compliant AI platform utilizing Databricks and Azure Cloud Services as the foundational backbone.
• Develop platform infra and services: Develop infrastructure and REST/Websocket-based AI/LLM platform services for consumption by different downstream applications.
• Own ML/LLM ops: Apply standardized architecture patterns and MLOps/LLMOps practices for model lifecycle management, observability, and security.
• Data Engineering: Develop efficient and scalable data pipelines to support AI/ML data requirements.
• GenAI Solution Development: Build and maintain robust RAG (Retrieval-Augmented Generation) and Agentic orchestration layers using tools like LangGraph and others to enable complex, multi-step GenAI workflows.
• Evaluate Emerging Technology: Proactively identify and evaluate emerging Generative AI technologies and integrate those that drive demonstrable business value.
• Technical Mentorship: Mentor and educate broader engineering teams on state-of-the-art Generative AI techniques and platform capabilities.

Qualifications and Skills

• Bachelor's degree in Computer Science, Machine Learning, Data Science, or related field.
• 3+ years of hands-on experience in developing, deploying, and maintaining GenAI or advanced ML models in production environments.
• 3+ years of experience in Python and GenAI frameworks/tools e.g. Databricks Vector Search, Azure AI Search, Azure AI document intelligence, LangGraph, haystack, Llama Index etc.
• Advanced knowledge of prompt engineering, model fine-tuning, embedding models, and effective RAG implementation methodologies, including vector databases, chunking strategies, GraphRAG etc.
• Deep familiarity with MLOps/LLMOps tools (e.g. MLflow), data platforms (e.g. Databricks) and cloud platforms.
• Demonstrated experience developing and deploying RESTful services, containerization, and automated CI/CD systems.
• Working knowledge of ML libraries e.g. PyTorch, TensorFlow, Hugging Face Transformers.
• Excellent communication and collaboration skills; proven ability to influence and partner with technical and non-technical stakeholders.

SMBC's employees participate in a Hybrid workforce model that provides employees with an opportunity to work from home, as well as, from an SMBC office. SMBC requires that employees live within a reasonable commuting distance of their office location. Prospective candidates will learn more about their specific hybrid work schedule during their interview process. Hybrid work may not be permitted for certain roles, including, for example, certain FINRA-registered roles for which in-office attendance for the entire workweek is required. SMBC provides reasonable accommodations during candidacy for applicants with disabilities consistent with applicable federal, state, and local law. If you need a reasonable accommodation during the application process, please let us know at View email address on click.appcast.io.