Applied AI Engineer

C&W Services is seeking an Applied AI Engineer to design, prototype, and operationalize applied AI and analytics solutions that automate work and strengthen decision-making and performance. This role partners closely with operational leaders, collaborates with data engineering to scale solutions into production, and evaluates business impact to ensure results translate into day-to-day value.

Based in Chicago, IL, this position is onsite and supports initiatives that move from proof of concept to reliable deployment within a modern analytics environment.

What you will do

• Design, build, and iterate applied AI and machine learning solutions for practical operational needs, including forecasting, classification, anomaly detection, NLP, and generative AI or LLM-based workflows.
• Independently deliver end-to-end POCs to validate AI and analytics ideas quickly, including standing up the data, modeling, and lightweight infrastructure to demonstrate value before production investment.
• Work with data engineering to harden, scale, and operationalize proven solutions by integrating them into operational systems, ensuring reliability and observability, and supporting iteration after deployment.
• Define success metrics, design offline and online evaluations, and quantify business impact.
• Create feedback loops to detect drift, regression, or misuse and respond appropriately.
• Analyze operational data, workflows, and performance trends to identify where AI and automation can deliver measurable value and surface actionable insights for service delivery and efficiency.
• Collaborate directly with Geography or Vertical leadership and frontline operators to understand workflows, decision points, and constraints.
• Translate operational problems into well-scoped AI and analytics solutions, and convert technical findings into clear, actionable guidance.
• Prepare, clean, and structure datasets for analytics and AI workflows, including engineering features and supporting retrieval strategies for LLM-based systems.
• Develop, test, and deploy analytics and AI solutions within the Databricks Lakehouse environment provided by data engineering.
• Apply software engineering practices such as version control, testing, code review, and modular design to make prototypes easier to harden and maintain.
• Pilot, refine, and support adoption of AI tools with field and operational teams, iterating based on feedback, evaluation results, and evolving business needs.
• Use practical judgment around model limitations, hallucinations, bias, privacy, and human-in-the-loop design to support trustworthy deployment in an operational context.

Required qualifications

• Bachelor’s degree in Analytics, Data Science, Computer Science, Engineering, or a related field.
• 4 to 7 years of experience in analytics, data science, or AI/ML engineering, including at least 2 years building and deploying ML or AI solutions.
• Strong proficiency in Python and SQL, including writing maintainable, tested code beyond exploratory notebooks.
• Hands-on experience building applied AI or ML solutions (predictive models, NLP, or LLM-based applications), not only conceptual familiarity.
• Ability to build end-to-end POCs independently, including data wrangling, modeling, and lightweight infrastructure to show value quickly.
• Experience partnering with data engineering or platform teams to take prototypes into production.
• Experience working with large datasets in modern analytics platforms such as Databricks.
• Demonstrated ability to translate operational problems into analytical and AI approaches that deliver measurable business outcomes.
• Strong communication skills with non-technical stakeholders, including making AI behavior, limitations, and results understandable.

Technologies

• Python
• SQL
• Databricks
• Databricks Lakehouse
• MLflow

Salary and location

• Location: Chicago, IL (onsite)
• Compensation: USD 85,000 - 100,000 per year

Benefits

• Health, vision, and dental insurance
• Flexible spending accounts
• Health savings accounts
• Retirement savings plans
• Life and disability insurance programs
• Paid and unpaid time away from work
• Competitive pay

Preferred qualifications

• Production experience with generative AI, LLM APIs (e.g., OpenAI, Anthropic), RAG systems, or agentic workflows
• Familiarity with MLOps tooling and practices (e.g., MLflow, model registries, CI/CD for ML, monitoring/observability)
• Experience designing evaluation frameworks for AI systems, including offline benchmarks and online experimentation
• Experience in operational, services, or asset-heavy environments
• Exposure to predictive modeling, time series analysis, or NLP in business contexts
• Familiarity with Databricks Lakehouse concepts and collaborative analytics workflows
• Track record of driving adoption of analytics or AI tools within business operations, including process and change-management considerations
• Ability to work independently while managing multiple concurrent initiatives