AI/ML & Forward Deployed Engineer

Job Details

• Job Title: AI/ML & Forward Deployed Engineer
• Location: Minnetonka Mills, MN
• Duration: 6 months
• Experience: 8+ years
• Primary Focus: AI/ML, GenAI, Software Engineering, Cloud & Forward Deployment
• Key Skills:
  • Microsoft Azure
  • AI Agents
  • Core .NET Technologies
  • Microsoft SQL Server 2019

Role Overview

• Deliver high-impact AI/ML and GenAI solutions end-to-end.
• Combine expertise in:
  • Applied Machine Learning
  • Software Engineering
  • Generative AI
  • Cloud Technologies
  • Stakeholder Engagement
• Build and deploy production-grade systems that are:
  • Scalable
  • Secure
  • Observable
  • Business-focused
• Work at the intersection of data, models, systems, and real users.
• Operate effectively in ambiguous and fast-moving environments.

Key Responsibilities

Use-Case Discovery & Forward Deployment

• Partner with business, product, and customer stakeholders to identify AI opportunities.
• Define AI use cases with:
  • Success metrics
  • Business constraints
  • Rollout plans
• Conduct technical discovery and workshops.
• Assess:
  • Technical feasibility
  • Data readiness
  • Integration requirements
  • Operational risks
• Drive rapid prototyping and pilot deployments.
• Improve solutions iteratively based on real-user feedback.

Applied ML Engineering

• Develop and optimize ML solutions for:
  • Classification
  • Regression
  • Ranking
  • Forecasting
  • Anomaly Detection
  • NLP
• Establish robust model evaluation practices.
• Implement:
  • Offline metrics
  • Validation strategies
  • Experimentation
  • A/B testing
• Perform feature engineering and error analysis.
• Optimize models for production performance.

GenAI / LLM Engineering

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

Productionization / MLOps / LLMOps

• Package ML models and AI applications into scalable services.
• Deploy solutions using:
  • Docker
  • Kubernetes
  • CI/CD
• Implement model lifecycle management.
• Manage:
  • Model registries
  • Versioning
  • Automated retraining
  • Governance workflows
• Build monitoring and observability for:
  • Model drift
  • Latency
  • Throughput
  • Errors
  • Alerts
  • Rollbacks

Systems Integration

• Build REST/gRPC APIs and event-driven services.
• Integrate AI capabilities into enterprise applications and workflows.
• Collaborate with Data Engineering teams on reliable data pipelines.
• Ensure:
  • Data quality
  • Data lineage
  • Data governance
• Implement secure and compliant solutions covering:
  • PII/PHI
  • RBAC
  • Secrets Management
  • Encryption
  • Audit Trails

Technical Leadership

• Provide technical guidance and mentoring to engineers.
• Lead design and architecture reviews.
• Establish engineering best practices.
• Create reusable:
  • Templates
  • Libraries
  • Frameworks
  • Deployment patterns
• Prepare architecture diagrams, runbooks, and operational documentation.

Required Technical Skills

Programming & Frontend

• Python – Primary
• ReactJS – Primary UI technology
• Angular – Alternative frontend technology
• .NET / .NET Core
• C#
• MVC
• Entity Framework
• Web API
• Web Services

API & Backend

• REST API development
• Spring Boot / FastAPI / Node.js
• RESTful APIs
• Microservices
• OAuth2
• JWT
• API Gateways:
  • Azure API Management
  • Apigee
• JSON
• XML
• HTTP
• gRPC

AI / ML / GenAI

• AI/ML
• Generative AI
• AI Agents
• LLM Integration
• Azure OpenAI
• OpenAI APIs
• LangChain
• Semantic Kernel
• RAG
• Prompt Engineering
• Embeddings
• Vector Databases
• Pinecone
• Azure AI Search

Cloud & Infrastructure

• Azure – Preferred
  • Azure App Services
  • Azure Functions
  • Azure Kubernetes Service (AKS)
  • Azure Storage / Blob
  • Azure Cosmos DB
• AWS – Secondary
  • Lambda
  • ECS/EKS
  • S3
• Cloud-native architecture
• Docker
• Kubernetes

Data Engineering

• SQL RDBMS
• SQL Server
• MySQL
• Oracle
• T-SQL
• Data Modeling
• Database Performance Monitoring
• Database Performance Tuning
• NoSQL
• MongoDB
• Cosmos DB

.NET Technical Requirements

• 7+ years of experience with .NET technologies.
• Strong experience with:
  • .NET Core
  • MVC
  • Entity Framework
  • Web API
  • Web Services
  • C#
• 2+ years of Azure Cloud experience.
• 4+ years of frontend development experience, preferably Angular.
• 5+ years of experience with SQL Server, MySQL, Oracle, or comparable RDBMS.
• 3+ years of experience with:
  • Data Modeling
  • T-SQL
  • Database Performance Monitoring
  • Database Performance Tuning
• 5+ years of experience with APIs and Microservices.
• 2+ years of experience with CI/CD tools such as:
  • Jenkins
  • GitHub Actions
• 2+ years of experience with code security and quality tools covering:
  • Vulnerability scanning
  • Code quality
  • Secret scanning
  • Penetration testing
  • Threat modeling
• 2+ years of experience with unit testing frameworks.

Preferred Qualifications

• Forward-deployed or customer-embedded engineering experience.
• Consulting, solutions engineering, or implementation engineering experience.
• Infrastructure as Code (IaC):
  • Terraform
  • ARM Templates
  • Bicep
• Experience with vector/search technologies:
  • Azure AI Search
  • Elasticsearch
  • OpenSearch
  • Pinecone
  • Weaviate
  • Milvus
• Experience with:
  • Databricks
  • Spark
  • MLflow
  • Kubeflow
  • Azure ML
  • SageMaker
  • Vertex AI
• Responsible AI experience:
  • Model Governance
  • Fairness Testing
  • Explainability
  • Audit Readiness
• Healthcare / PBM domain experience.

Core Skills

• Software Development: Programming and database development
• Machine Learning: Training, evaluation, feature engineering, error analysis, model serving
• GenAI: RAG, retrieval tuning, prompt orchestration, guardrails, evaluation
• Software Engineering: APIs, Microservices, Integration, Performance Optimization
• MLOps / LLMOps: CI/CD, Monitoring, Drift Detection, Versioning, Rollout/Rollback
• Cloud & Platform: Compute, Storage, IAM, Networking, Containers, Kubernetes
• Security: Secrets Management, RBAC, Encryption, Compliance

Success Metrics

• AI/ML solutions successfully deployed to production with clear SLOs.
• Improvements in:
  • Automation rate
  • Cost reduction
  • Cycle time
  • Conversion/retention
  • Defect reduction
• High stakeholder adoption and satisfaction.
• Reusable deployment patterns that reduce implementation friction.
• Strong operational performance with:
  • Comprehensive monitoring
  • Fast incident response
  • Low failure rates