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

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