AI/ML Engineer

AI/ML Engineer

Location: Minnetonka Mills, MN

Duration: 6 months

Experience: 4–6 years

Primary Focus: Machine Learning, NLP, LLMs, GenAI, MLOps

Core Skills: Python, TensorFlow, PyTorch, Docker, REST APIs, Databricks

Role Summary

Build, train, tune, and deploy machine learning models.

Translate data science experiments and prototypes into scalable, production-ready ML solutions.

Develop reliable ML services and pipelines for enterprise production environments.

Key Responsibilities

Translate data science prototypes into production-grade ML services and pipelines.

Build training and inference code with:

• Reproducibility
• Versioning
• Automated testing

Implement scalable online and offline model serving.

Optimize model serving for:

• Batching
• Latency
• Throughput
• Scalability

Integrate ML lifecycle tooling, including:

• Experiment tracking
• Model registries
• Deployment automation
• Model monitoring

Collaborate with Data Engineering teams on feature pipelines and data contracts.

Own production ML health, including:

• Drift detection
• Performance regression
• Rollback strategies
• Incident response

Machine Learning & Data Science

Experience applying data science techniques in a commercial enterprise environment.

Strong understanding of:

• Supervised Learning
• Unsupervised Learning
• NLP
• Time-Series Forecasting
• Statistical Analysis

Experience with large-scale data and production ML workloads.

Experience with scikit-learn and Databricks.

Strong analytical, quantitative, problem-solving, and critical-thinking skills.

NLP & LLM / GenAI

3+ years of production experience with NLP, including:

• Transformers
• GPT
• Other modern NLP technologies

2–3 years of experience applying LLMs to real-world business problems.

Hands-on experience with:

• RAG (Retrieval-Augmented Generation)
• Vector Databases
• Embeddings

Experience using vision and speech models in GenAI applications.

Experience working with large amounts of data for NLP and LLM solutions.

MLOps & Production Engineering

5+ years of software engineering experience.

2+ years of experience shipping ML models to production.

Strong understanding of MLOps and ML system design.

Experience with:

• CI/CD
• DevOps
• Model deployment
• Model monitoring
• Model versioning

Understanding of ML production challenges, including:

• Data leakage
• Training-serving skew
• Model drift
• Performance degradation

Development & Cloud Technologies

Strong Python development skills.

Experience with ML frameworks:

• TensorFlow
• PyTorch

Experience with:

• Docker
• Kubernetes
• REST APIs

Experience building APIs for machine learning models.

Experience with scalable model serving and distributed ML workloads.

Preferred / Nice-to-Have Skills

Experience with feature stores.

Experience with model registries.

Experience with model monitoring platforms.

GPU optimization experience.

Distributed training experience.

Responsible AI toolkits and compliance experience.

Soft Skills

Strong written and verbal communication skills.

Ability to present detailed technical analyses to broad audiences.

Organized, self-motivated, and able to work independently.

Strong analytical and problem-solving abilities.

Essential Skills

AI/ML Engineering

Python

TensorFlow

PyTorch

NLP

LLMs

RAG

Vector Databases

Embeddings

Databricks

scikit-learn

Docker

Kubernetes

REST APIs

MLOps

CI/CD

Digital Skills

Machine Learning

Natural Language Processing (NLP)

AI & GenAI – Products & Tools