Data & AI Engineer – Equities Technology

Solutions for Today’s Challenges. Vision for Tomorrow’s Opportunities.

Join William Blair, the Premier Global Partnership.

We’re seeking a Data & AI Engineer to design, build, and maintain intelligent, scalable data and AI‑enabled platforms supporting our Equities business across Trading, Research, and Sales. This role spans the full software development lifecycle and is responsible for moving data and AI solutions from concept through production, while ensuring reliability, security, and compliance with firm standards.

The ideal candidate brings strong data engineering fundamentals combined with hands‑on experience integrating AI and LLM‑based capabilities into production systems in a regulated environment.

The role will be based in Chicago with a hybrid work schedule.

Key Responsibilities

Data Engineering & Platform Foundations

• Design, build, and maintain scalable data pipelines using Databricks, Azure Data Factory, and Azure Synapse

• Implement robust ETL/ELT workflows for structured and unstructured financial data across on‑prem and cloud platforms

• Ensure data quality, lineage, governance, security, and observability across all pipelines and storage layers

• Design and optimize data models and analytical schemas (star/snowflake, partitioning, distribution strategies)

• Build reusable ingestion and transformation frameworks to support analytics and AI workloads

AI Integration & Agentic Workflows

• Build and deploy AI‑enabled services, agents, and workflows supporting equity research, trading, sales, and client service use cases

• Implement LLM‑based and agentic patterns, including Retrieval‑Augmented Generation (RAG), using proprietary firm data

• Integrate AI capabilities into existing applications and platforms via APIs, batch jobs, and event‑driven workflows

• Partner with Data Science and business stakeholders to translate AI concepts into production‑ready solutions

AI Enablement & Productionization

• Productionalize AI and Data Science POCs into secure, scalable, and monitored services suitable for regulated environments

• Optimize prompts, embeddings, orchestration logic, and inference workflows for accuracy, performance, cost, and reliability

• Ensure AI solutions meet firm standards for security, auditability, explainability, and compliance

• Establish operational practices for AI solutions, including monitoring, alerting, lifecycle management, and runbooks