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
