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