AI/ML & Forward Deployed Engineer

• Job Title: AI/ML & Forward Deployed Engineer
• Location: Minnetonka Mills, MN
• Duration: 6 months
• Experience: 8 years
• Required Experience: 6-8 years
• Primary Focus: AI/ML, GenAI, Forward Deployment, Software Engineering, MLOps/LLMOps
• Core Skills: Python, React.js, REST APIs, Microservices, Azure, Deep Learning, Generative AI, AI Agents
  
  

Role Overview

• Deliver end-to-end AI/ML and GenAI solutions.
• Combine applied machine learning, software engineering, and stakeholder problem-solving.
• Build production-grade systems that are:
• Scalable
• Secure
• Observable
• Reliable
• Aligned with business KPIs
• Work at the intersection of data, models, systems, and real users.
• Thrive in ambiguous and fast-moving environments.
  
  

Use-Case Discovery & Forward Deployment

• Partner with business, product, and customer stakeholders to identify and define AI opportunities.
• Translate business requirements into AI use cases with:
• Success metrics
• Technical constraints
• Rollout plans
• Conduct workshops and technical discovery covering:
• Feasibility
• Data readiness
• Integration requirements
• Operational risks
• Drive rapid prototyping, pilot deployments, and iterative improvements based on user feedback.
• Experience with forward-deployed/customer-embedded delivery is preferred.
  
  

Applied ML Engineering

• Develop and improve ML solutions for:
• Classification
• Regression
• Ranking
• Forecasting
• Anomaly detection
• NLP
• Perform feature engineering, error analysis, model optimization, and performance tuning.
• Establish robust ML evaluation practices, including:
• Offline metrics
• Validation strategies
• Experimentation
• A/B testing
• Experience with deep learning and production ML systems.
  
  

GenAI / LLM Engineering

• Build and productionize RAG (Retrieval-Augmented Generation) pipelines.
• Work with:
• Document ingestion
• Chunking strategies
• Embeddings
• Retrieval tuning
• Reranking
• Response grounding
• Implement GenAI guardrails and reliability patterns:
• Prompt templates
• Tool/function calling
• Hallucination reduction
• Citation strategies
• Fallback mechanisms
• Build GenAI evaluation frameworks covering:
• Quality metrics
• Regression testing
• Safety testing
• Human-in-the-loop workflows
• Experience with LLMs, Generative AI, and AI Agents.
  
  

MLOps / LLMOps & Productionization

• Package ML/LLM models into scalable services using Docker and Kubernetes.
• Implement CI/CD pipelines for AI/ML workloads.
• Manage the complete model lifecycle, including:
• Model registry
• Versioning
• Automated retraining
• Governance workflows
• Build monitoring and observability for:
• Model drift
• Latency
• Throughput
• Errors
• Alerts
• Rollbacks
• Apply DevOps and CI/CD best practices to ML workloads.
  
  

API & Backend Engineering

• Develop scalable REST and gRPC APIs.
• Build event-driven services and microservices.
• Experience with:
• Spring Boot
• FastAPI
• Node.js
• Implement API integrations using:
• OAuth2
• JWT
• API Gateways
• Experience with Azure API Management and Apigee.
• Work with JSON and XML data formats.
• HL7/FHIR experience is preferred for healthcare environments.
  
  

Frontend Development

• Strong React.js experience is preferred.
• Angular experience may be considered as an alternative.
• Build and integrate AI-enabled user-facing applications.
  
  

AI/ML & GenAI Technologies

• Experience with:
• Azure OpenAI
• OpenAI APIs
• LangChain
• Semantic Kernel
• RAG
• Prompt Engineering
• Embeddings
• Vector Databases
• Experience with vector/search platforms:
• Pinecone
• Azure AI Search
• Elasticsearch/OpenSearch
• Weaviate
• Milvus
  
  

Azure Cloud & Infrastructure

• Strong Azure experience preferred.
• Experience with:
• Azure App Services
• Azure Functions
• Azure Kubernetes Service (AKS)
• Azure Storage / Blob
• Azure Cosmos DB
• Azure Machine Learning
• Secondary AWS experience:
• AWS Lambda
• ECS/EKS
• S3
  
  

Data Engineering

• Experience with SQL and relational databases.
• Experience with NoSQL databases, preferably MongoDB or Cosmos DB.
• Collaborate with Data Engineering teams to build reliable data pipelines.
• Ensure:
• Data quality
• Data lineage
• Data governance
  
  

Security & Compliance

• Design secure and compliant AI/ML solutions.
• Experience handling:
• PII
• PHI
• RBAC
• Secrets management
• Encryption
• Audit trails
• Apply security and compliance considerations throughout the development lifecycle.
  
  

Platform & MLOps Tools

• Experience with:
• Databricks / Spark
• MLflow
• Kubeflow
• Azure ML
• SageMaker
• Vertex AI
• Infrastructure-as-Code experience:
• Terraform
• ARM Templates
• Bicep
  
  

Technical Leadership

• Provide technical guidance and mentorship to engineering teams.
• Lead architecture and design reviews.
• Establish engineering and AI development best practices.
• Create reusable:
• Templates
• Libraries
• Patterns
• Accelerators
• Document solutions through:
• Architecture diagrams
• Runbooks
• Operational playbooks
  
  

Domain Experience

• Healthcare or PBM (Pharmacy Benefit Management) experience is preferred.
• HL7/FHIR knowledge is a nice-to-have.
  
  

Essential Skills

• AI/ML
• Forward Deployed Engineering
• Python
• Deep Learning
• Generative AI
• AI Agents
• React.js
• Microservices
• REST APIs
• Spring Boot
• Azure
• Azure Machine Learning
• MLOps / LLMOps
• Docker / Kubernetes
• CI/CD
• RAG
• LLMs
• Vector Databases
• API Integration
• Data Engineering
  
  

Digital Skills

• Deep Learning
• DevOps / CI/CD
• ReactJS
• Microservices
• Spring Boot
• Azure Machine Learning (ML)
• Generative AI
• AI Agents