AI/ML & Forward Deployed Engineer
Job in
Minnetonka Mills, Hennepin County, Minnesota, USA
Listed on 2026-07-01
Listing for:
Diverse Lynx
Full Time
position Listed on 2026-07-01
Job specializations:
-
IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Job Description & How to Apply Below
Job Title: AI/ML & Forward Deployed Engineer
Location: Minnetonka Mills, MN
Duration:
Contract/w2
Rate :
- $40-$50 /hr on C2C
Key Responsibilities Use Case Discovery & Forward Deployment
- Partner with business stakeholders, product teams, and customers to identify AI/ML opportunities.
- Translate business problems into clearly defined AI use cases with measurable success metrics.
- Conduct technical discovery sessions to evaluate:
- Data readiness
- Solution feasibility
- Integration requirements
- Operational risks
- Lead workshops and customer engagements to understand business requirements.
- Develop rapid prototypes and proof of concepts (PoCs).
- Drive pilot deployments and iterate solutions based on user feedback.
- Ensure successful production deployment and user adoption of AI solutions.
- Design, develop, and deploy machine learning models for:
- Classification
- Regression
- Ranking
- Forecasting
- Anomaly Detection
- Natural Language Processing (NLP)
- Perform feature engineering and data preprocessing.
- Optimize model performance for production environments.
- Conduct error analysis and continuous model improvement.
- Implement model validation techniques and evaluation metrics.
- Design and execute A/B testing and experimentation frameworks.
- Monitor model performance and ensure reliability in production.
- Design and build Retrieval-Augmented Generation (RAG) solutions.
- Develop document ingestion and indexing pipelines.
- Implement:
- Chunking strategies
- Embedding generation
- Semantic search
- Retrieval optimization
- Re-ranking techniques
- Build prompt engineering frameworks and reusable prompt templates.
- Implement LLM tool/function calling capabilities.
- Design guardrails to reduce hallucinations and improve response quality.
- Develop citation and grounding mechanisms for reliable AI responses.
- Create fallback strategies to improve system resilience.
- Build scalable, secure, and production-ready AI applications.
- Deploy AI/ML solutions using Azure Machine Learning and cloud-native services.
- Implement MLOps best practices for:
- Model deployment
- Versioning
- Monitoring
- CI/CD pipelines
- Ensure application observability, logging, and performance monitoring.
- Collaborate with Dev Ops teams for production releases.
- Strong experience in Artificial Intelligence and Machine Learning.
- Hands-on experience with:
- Python
- Machine Learning
- Deep Learning
- Natural Language Processing (NLP)
- Generative AI
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Experience with Azure Machine Learning (Azure ML).
- Strong understanding of:
- Prompt Engineering
- Embeddings
- Vector Databases
- Semantic Search
- Model Evaluation
- Feature Engineering
- Experience with cloud deployment and production AI systems.
- Knowledge of MLOps, CI/CD, Docker, and Kubernetes is preferred.
- Strong analytical and problem-solving skills.
- Excellent communication and stakeholder management abilities.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field.
- Experience building enterprise-scale AI/ML applications.
- Experience deploying customer-facing AI solutions.
- Familiarity with Azure AI services and cloud-native architectures.
- Experience working in Agile/Scrum environments is preferred.
- 8+ years of software engineering or AI/ML engineering experience.
- Proven experience delivering end-to-end AI/ML solutions in production.
- Hands-on experience with Generative AI and LLM-based applications.
- Experience deploying scalable AI systems in enterprise environments.
- Strong background in customer-facing or forward-deployed engineering roles.
- Azure Machine Learning (Azure ML)
- Deep Learning
- Generative AI
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Python
- NLP
- Prompt Engineering
- MLOps
- Docker
- Kubernetes
- CI/CD
- Model Deployment
- Stakeholder Management
- Solution Architecture
- AI Strategy & Consulting
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