Principal AI Solutions Engineer

About the role

We are looking for a Principal AI Solutions Engineer to design, implement, and optimize AI/LLM solutions that drive business value across Brokerage Services Dev.

Responsibilities

• Data Platform & BI Integration: Help develop and maintain data models, SQL queries, and analytics workflows in Databricks. Support BI reporting infrastructure including Power BI and Sigma integrations. Implement data quality monitoring, anomaly detection, and automated alerting systems. Partner with EA/Platform teams on data pipeline development and optimization.
• Technical Architecture & Platform Development: Architect scalable AI solutions leveraging Databricks, Unity Catalog, and modern data platforms. Help design and implement data pipelines, feature engineering workflows, and ML infrastructure. Establish technical patterns and best practices for AI/LLM system development. Build tooling and frameworks that accelerate AI solution delivery across teams.
• AI/LLM Solution Engineering: Design and implement production-grade AI/LLM systems including RAG pipelines, prompt engineering frameworks, and evaluation workflows. Build and optimize MCP integrations, AI agent architectures, and LLM orchestration patterns. Develop guardrails, observability systems, and monitoring solutions for AI/LLM applications. Work hands-on with model deployment, fine-tuning, and performance optimization.
• Business Requirements & Solution Design: Partner with business stakeholders to translate requirements into technical solutions. Conduct technical discovery, assess feasibility, and define solution architectures. Create technical specifications, design documents, and implementation plans. Collaborate with Data Science and ML Engineering teams on model development and deployment.
• Operational Excellence: Establish observability and monitoring for production AI systems. Implement cost tracking and optimization strategies for compute and serverless resources. Build experimentation frameworks (A/B testing, pilots) and evaluation methodologies. Drive continuous improvement through performance analysis and system optimization.
• Governance & Risk Management: Implement responsible AI practices including safety, fairness, and privacy controls. Develop model risk management processes and documentation. Establish access governance patterns for Databricks resources and AI platforms. Create technical documentation, runbooks, and knowledge-sharing materials.

Requirements

• Strong software engineering fundamentals with experience building production systems.
• Deep technical expertise in AI/LLM technologies, including prompt engineering, RAG systems, and agent frameworks.
• Hands-on experience with Databricks platform (SQL Warehouses, Unity Catalog, MLflow) and data engineering.
• Proficiency in Python, SQL, and modern ML/AI frameworks and libraries.
• Experience with cloud platforms and infrastructure as code.
• Strong understanding of data modeling, pipeline development, and analytics workflows.
• Familiarity with BI tools (Power BI, Sigma) and data visualization.
• Experience with Agile development practices and tools (Git, Jira, CI/CD).
• Knowledge of experimentation methodologies, A/B testing, and statistical analysis.
• Understanding of responsible AI principles, model risk management, and governance.
• Excellent communication skills with ability to explain technical concepts to business stakeholders.
• Strong problem-solving ability and experience working in fast-paced environments.
• Prioritized hands-on experience with Databricks, modern data platforms, and cloud infrastructure.
• Proven track record building and deploying production AI/LLM applications.
• Deep hands-on experience with modern data platforms including data lakes, Delta Lake, Unity Catalog, and Lakehouse architectures preferred.
• Proven track record building and scaling RAG systems in production environments preferred.
• Experience implementing Model Context Protocol (MCP) servers and integrations preferred.
• Experience with prompt engineering frameworks, evaluation systems, and LLM observability tools preferred.
• Familiarity with AI governance frameworks and responsible AI implementation in enterprise settings preferred.
• Published work, open-source contributions, or conference presentations related to AI/ML systems preferred.
• Experience with real-time data processing and stream processing frameworks (Kafka, Spark Streaming) preferred.
• Knowledge of cost optimization strategies for cloud-based ML workloads and serverless architectures preferred.

Qualifications

• 4+ years of experience in software engineering, ML engineering, data engineering, or related technical roles with significant focus on AI/ML systems.
• Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field; equivalent experience considered.
• 7+ years of experience in software engineering, ML engineering, or data engineering with at least 3 years focused on production AI/LLM systems.
• Master's degree or PhD in Computer Science, Machine Learning, Data Science, or related technical field.
• Databricks Certified Machine Learning Professional or Data Engineer Professional certification.
• Cloud platform certification (AWS Solutions Architect, Azure AI Engineer, or Google Cloud Professional Machine Learning Engineer).