Senior ML Engineer: Scale AI with LLMs & Data Tools

Zotec Partners; Carmel (IN); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

PTO

Health/dental/vision insurance

Life/AD&D/ disability insurance

401(k) contribution

Zotec Effect program

Learning & development opportunities

Job summary

Zotec Partners seeks a Senior Machine Learning Engineer to build and extend AI systems enabling governed data access. This onsite role in Carmel, IN focuses on shipping models, connecting LLMs to Zotec data, and improving safe AI reasoning over that data.

You will own the ML lifecycle, design scalable infrastructure on AWS, and apply knowledge-graph approaches to improve data retrieval and reasoning in production systems.

Qualifications

• Bachelor's degree in a STEM field required; Master's preferred.
• 3–5 years delivering ML solutions at scale.
• Proficiency in Python and ML model development.
• Experience with structured and unstructured data at scale.

Responsibilities

• Design, develop, and maintain ML models and data integrations for AI-driven products and internal tools.
• Connect LLMs to real data with tools for accurate, safe querying and reasoning.
• Own ML lifecycle: framing problems, data prep, training, evaluation, deployment, and monitoring.
• Architect cost-aware ML infra on AWS using SageMaker, Bedrock, and Lambda.
• Apply ontology/knowledge-graph thinking to improve retrieval, reasoning, or data quality.

Skills

Python

ML model development

Data engineering concepts

Cross-functional collaboration

Education

Bachelor's degree in STEM

Master's degree preferred

Tools

AWS (SageMaker Bedrock Lambda Redshift)

Claude & LLMs

Ontology & knowledge graphs

CI/CD / IaC

Job description

Zotec Partners seeks a Senior Machine Learning Engineer to build and extend AI systems enabling governed data access. This onsite role in Carmel, IN focuses on shipping models, connecting LLMs to Zotec data, and improving safe AI reasoning over that data.

You will own the ML lifecycle, design scalable infrastructure on AWS, and apply knowledge-graph approaches to improve data retrieval and reasoning in production systems.