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