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