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, Tensor Flow, 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 LearningNLPTime-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 / GenAI3+ years of production experience with NLP, including: TransformersGPTOther 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/CDDev Ops 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: Tensor Flow PyTorch Experience with: Docker KubernetesREST APIsExperience 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 SkillsAI/ML Engineering Python Tensor Flow PyTorchNLPLLMsRAGVector Databases Embeddings Databricksscikit-learn Docker KubernetesREST APIsMLOpsCI/CDDigital Skills Machine Learning Natural Language Processing (NLP)AI & GenAI - Products & Tools