Machine Learning (ML) / Data Engineer

1w1 week ago

Moser Consulting

Indianapolis, US · Full-time · $120,000 – $155,000

About this role

We are seeking an AI/ML/Data engineer with several years of technical experience building production-grade solutions. This role blends AI/ML engineering, data engineering, and software engineering to support clients across a variety of industries. You will deliver within cloud, on-prem, or hybrid environments to engineer, deploy, and maintain end-to-end AI/ML systems.

Design, implement and deploy production-grade machine learning models and systems using modern MLOps practices, spanning from classical ML to Gen AI. Prepare datasets, feature pipelines, evaluation scaffolding, experiment tracking, and model packaging. Implement model inference services, deployment workflows, and monitoring mechanisms across data and model layers.

Build ingestion, transformation, and storage pipelines for analytical and ML workflows using SQL and Python data tooling. Ensure data quality and integrity by implementing data validation and cleansing processes. Write modular, testable, and maintainable codebases, build APIs, and use containers, CI/CD, and automated testing for reliability.

Collaborate with a technical lead, engineers, analysts, and domain stakeholders while building reusable patterns and contributing to a growing Data Intelligence capability. Work in a collaborative and fast-paced environment where consultants are self-motivated and passionate about evolving technology. Moser Consulting is recognized as one of the Best Places to Work in Indiana for 10 consecutive years.

Requirements

Several years of technical experience building production-grade solutions
Experience blending AI/ML engineering, data engineering, and software engineering
Proficiency with modern MLOps practices for classical ML to Gen AI
Skills in SQL and Python data tooling for ETL/ELT pipelines
Ability to implement model governance, reproducibility standards, and versioned models
Knowledge of containers, CI/CD pipelines, and automated testing
Experience debugging, performance tuning, and failure analysis across data and model layers

Responsibilities

Design, implement and deploy production-grade machine learning models and systems using modern MLOps practices
Prepare datasets, feature pipelines, evaluation scaffolding, experiment tracking, and model packaging
Implement model inference services, deployment workflows, and monitoring mechanisms
Build ingestion, transformation, and storage pipelines for analytical and ML workflows
Ensure data quality and integrity by implementing data validation and cleansing processes
Develop scalable, optimized ETL/ELT pipelines using SQL and Python data tooling
Write modular, testable, and maintainable codebases that follow idiomatic patterns
Use containers, CI/CD, and automated testing to ensure reliability

Benefits

Recognized as one of the Best Places to Work in Indiana for 10 consecutive years
Collaborative and fast-paced environment for self-motivated employees
Model-Coach-Care philosophy emphasizing core values like Accountability, Collaboration, and Transparency
Commitment to a diverse, equitable, and inclusive culture