> Markdown version of [/jobs/ext/3190618-data-scientist-ai-engineer](https://www.wearedevelopers.com/jobs/ext/3190618-data-scientist-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - AI Engineer - **Company:** William Blair - **Location:** Chicago, IL, United States - **Experience:** Expert - **Salary:** $157,000.0 - $187,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Data Analysis, Microsoft Azure, Code Review, Continuous Integration, Machine Learning, Rapid Prototyping Process, Salesforce.Com, Software Engineering, Data Streaming, Feature Engineering, Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Production Code, Stream Analytics, Data Pipelines, Databricks - **Published:** September 2, 2026 - **Apply:** https://www.dice.com/job-detail/4ebf48dd-3118-4093-9855-97aed64c4538 ## About the Role * 3+ years of software engineering experience with full-stack capability and a track record of shipping production applications. * Experience building applications or agents using large language models: prompt engineering, RAG architectures, LLM orchestration, or tool-use patterns. * Experience building interconnected multi-agent ecosystems - implementing agent coordination, shared tooling, and communication patterns across autonomous components. * Solid ML fundamentals: ability to perform data analysis, build and evaluate models, and work with feature pipelines. * Rigorous engineering habits-you believe in tested code, clean architecture, and building for maintainability from the start. * Familiarity with capital markets; experience in or adjacent to investment banking, private equity, venture capital, or hedge funds is strongly preferred. * Experience with cloud platforms (Azure preferred), data tools (Databricks, Spark), and pipeline orchestration (Dagster, Airflow, or similar). * Outcome-focused mindset-you care about whether bankers actually use what you build and whether it moves the needle on their productivity. Preferred Qualifications: * Experience in a Forward Deployed Engineer, solutions engineer, or embedded technical role with direct business stakeholder accountability. * Experience deploying multi-agent ecosystems into production environments - including operational monitoring, failure handling, and end-to-end lifecycle management. * Exposure to financial services workflows: deal execution, pitch preparation, due diligence, or financial modeling. * Experience working with Salesforce APIs or CRM platforms as integration surfaces. * A builder's mentality: you have side projects, open-source contributions, or a portfolio that demonstrates curiosity and initiative beyond your day job. ## Description We are hiring a Senior AI Engineer to join a newly formed AI Innovation Function, part of the Investment Banking AI & Technology team. This is not a back-office engineering role. This role follows the Forward Deployed Engineer philosophy: you will not build in isolation. You will be embedded with deal teams and industry/sector groups, understanding their day-to-day workflows and delivering tools that create immediate, measurable impact on how they originate, execute, and close deals. You will work across the full stack-data pipelines, ML models, LLM-powered applications, and Salesforce integrations-with a bias for shipping fast, learning from real user feedback, and iterating relentlessly. Responsibilities include but may not be limited to: * Build AI-powered features and agents using Enterprise Claude and proprietary ML models, integrated directly into the Salesforce workflows bankers use every day. * Develop LLM applications for banking use cases including automated comparable analysis, buyer recommendation, meeting intelligence summarization, and deal status briefings. * Design and maintain data pipelines using Databricks and Dagster for feature engineering, model training, and real-time analytics. * Work directly with deal teams and industry groups to identify high-impact automation opportunities and translate banker pain points into working solutions. * Perform rapid prototyping and exploratory analysis-build proof-of-concept tools quickly to validate ideas before investing in production-grade implementations. * Integrate third-party AI tools (Rogo.ai, Blueflame AI, Fellow.ai) via APIs and ensure seamless data flows across the composable architecture. * Write well-tested, production-quality code with rigorous engineering practices: code reviews, CI/CD, monitoring, and documentation. * Contribute to the team's engineering standards and share knowledge as the team scales. ## Related Videos - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Are Code Reviews Worth It? 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