AI/ML & Forward Deployed Engineer

Location: Minnetonka Mills, MN | Duration: 6 months | Experience: 8+ years (required 6-8 years)

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, and aligned with business KPIs. Work at the intersection of data, models, systems, and real users. Thrive in ambiguous and fast-moving environments.

Responsibilities

• 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, and rollout plans.
• Conduct workshops and technical discovery covering feasibility, data readiness, integration requirements, and operational risks.
• Drive rapid prototyping, pilot deployments, and iterative improvements based on user feedback.
• Develop and improve ML solutions for classification, regression, ranking, forecasting, anomaly detection, and NLP; perform feature engineering, error analysis, model optimization, and performance tuning.
• Establish robust ML evaluation practices including offline metrics, validation strategies, experimentation, and A/B testing.
• Build and productionize RAG pipelines: 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, and human-in-the-loop workflows.
• Package ML/LLM models into scalable services using Docker and Kubernetes; implement CI/CD pipelines for AI/ML workloads.
• Manage the complete model lifecycle: model registry, versioning, automated retraining, governance workflows.
• Build monitoring and observability for model drift, latency, throughput, errors, alerts, and rollbacks.
• Develop scalable REST and gRPC APIs; build event-driven services and microservices using 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 preferred.
• Build and integrate AI-enabled user-facing applications with strong React.js experience (Angular considered as alternative).
• Collaborate with Data Engineering teams to build reliable data pipelines ensuring data quality, lineage, and governance.
• Design secure and compliant AI/ML solutions handling PII, PHI, RBAC, secrets management, encryption, and audit trails.
• 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, and accelerators; document solutions via architecture diagrams, runbooks, and operational playbooks.

Technical Skills

• Languages & Frameworks: Python, React.js (Angular alternative), Spring Boot, FastAPI, Node.js, REST APIs, gRPC.
• Cloud & Infrastructure: Azure (App Services, Functions, AKS, Storage/Blob, Cosmos DB, Azure Machine Learning), secondary AWS (Lambda, ECS/EKS, S3).
• MLOps / LLMOps: Docker, Kubernetes, CI/CD, MLflow, Kubeflow, Azure ML, SageMaker, Vertex AI, Terraform, ARM Templates, Bicep.
• AI/ML & GenAI Technologies: Azure OpenAI, OpenAI APIs, LangChain, Semantic Kernel, RAG, Prompt Engineering, Embeddings, Vector Databases (Pinecone, Azure AI Search, Elasticsearch/OpenSearch, Weaviate, Milvus), LLMs, Generative AI, AI Agents.
• Data Engineering: SQL, relational databases, NoSQL (MongoDB, Cosmos DB), data pipelines.
• Security & Compliance: PII, PHI, RBAC, secrets management, encryption, audit trails.
• Domain Experience: Healthcare or PBM preferred; HL7/FHIR knowledge nice-to-have.