AI/ML ENGINEER

100% OFFICE WORKING - EAST LANSING, MICHIGAN

SALARY $70K-$120K

Design, build, and deploy AI and machine learning systems that deliver measurable business value at our Financial Services client. As part of the AI & Automation team, contribute across the full engineering lifecycle—from data analysis and model development through production deployment. Depending on experience, this role may emphasize quantitative modeling alongside a senior data scientist, or production engineering of AI-powered systems. We are building a new team and hiring at multiple levels.

Core Responsibilities

• Design and implement AI solutions including document intelligence, knowledge search (RAG), and process automation
• Build and maintain production data pipelines: ingestion, transformation, feature engineering, and monitoring
• Develop APIs and services connecting AI capabilities to business applications
• Integrate and optimize LLM services: prompt engineering, RAG, evaluation, and cost management
• Collaborate on quantitative projects: pricing models, risk analysis, statistical modeling, and simulation
• Implement and evaluate ML models; support experimentation, validation, and performance analysis
• Build supporting data infrastructure: ETL pipelines, vector databases, data quality frameworks
• Contribute to AI platform architecture: cost tracking, usage monitoring, and access control
• Evaluate AI/ML tools, frameworks, and cloud services; recommend build-vs-buy decisions
• Create technical documentation and architecture diagrams
• Support AI governance and compliance in a regulated banking environment

Role Characteristics

• Reports to Head of AI & Automation
• In-office Monday–Friday; normal core hours are 9–4 with flexibility
• Balance rapid prototyping with production-quality engineering
• Work in sprints with clear deliverables using Lean and agile practices

Qualifications / Requirements

• BS or MS in Computer Science, Data Science, Mathematics, Statistics, or related quantitative field
• Strong Python skills; production systems or significant academic projects
• Experience or coursework in machine learning, statistical modeling, or applied mathematics
• Familiarity with ML frameworks (scikit-learn, PyTorch, TensorFlow) and data libraries (pandas, NumPy, SciPy)
• LLM integration, prompt engineering, and RAG experience valued
• Data pipeline experience (Airflow, dbt, or equivalent) a plus
• Familiarity with cloud AI services (Azure AI, AWS Bedrock) and containerization (Docker)
• Working knowledge of SQL; vector database experience a plus
• Understanding of API design and system integration patterns
• Effective communication skills for technical and non-technical audiences
• Regulated industry background (banking, healthcare, finance) is a significant plus
• Personal projects, research, open-source, or Kaggle experience valued