AI/ML Engineer

• Job Title: 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