AI/ML & Forward Deployed Engineer

Title: AI/ML & Forward Deployed Engineer (8+ Years)

Location: Minnetonka Mills, MN - Onsite

Duration: 6-12+ Months contract

Rate $60-65/hr with VMS charges

Job Description:-

Role Name: AI/ML & Forward Deployed Engineer (8+ Years)

Role Overview

We are looking for an experienced AI/ML & Forward Deployed Engineer with 8+ years of engineering experience to deliver high-impact AI/ML (and GenAI, where applicable) solutions end-to-end. You will blend applied machine learning, software engineering, and stakeholder problem-solving to deploy production-grade systems that are scalable, secure, observable, and aligned to business KPIs.

This role is ideal for engineers who enjoy operating at the intersection of data + models + systems + real users, and who can thrive in ambiguous, fast-moving environments

Key Responsibilities

1) Use-Case Discovery & Forward Deployment

• Partner with stakeholders (business/product/customers) to identify and shape AI opportunities into well-defined use cases with success metrics, constraints, and rollout plans.
• Run workshops and technical discovery to assess feasibility, data readiness, integration needs, and operational risks.
• Drive rapid prototyping, pilot deployments, and iterative improvements based on real user feedback.

2) Applied ML Engineering (Classic ML + Deep Learning)

• Develop and improve ML solutions (classification, regression, ranking, forecasting, anomaly detection, NLP).
• Establish and maintain robust evaluation practices: offline metrics, validation strategies, experimentation, and A/B testing.
• Perform feature engineering, error analysis, model Eligible to workimization, and performance tuning for production requirements.

3) GenAI / LLM Engineering (If Applicable)

• Build and productionize RAG (Retrieval-Augmented Generation) pipelines, including document ingestion, chunking strategy, embeddings, retrieval tuning, reranking, and response grounding.
• Implement guardrails and reliability patterns: prompt templates, tool/function calling, hallucination reduction, citation strategies, and fallback paths.
• Develop evaluation harnesses for GenAI: quality metrics, regression tests, safety tests, and human-in-the-loop workflows.

4) Productionization (MLOps / LLMOps)

• Package models into scalable services and deploy using Docker/Kubernetes and CI/CD.
• Implement model lifecycle management: model registry, versioning, automated retraining triggers, and governance workflows.
• Build monitoring and observability: drift detection, latency/throughput monitoring, error tracking, alerting, and rollback mechanisms.

5) Systems Integration & Platform Collaboration

• Build integration layers (REST/gRPC APIs, event-driven services) to embed AI capabilities into products and enterprise workflows.
• Collaborate with data engineers to design reliable pipelines and ensure data quality, lineage, and governance.
• Ensure secure and compliant design (PII/PHI handling, RBAC, secrets management, encryption, audit trails).

6) Technical Leadership & Enablement

• Provide technical guidance and mentoring to engineers; lead design reviews and establish best practices.
• Document solutions with architecture diagrams, runbooks, and operational playbooks.
• Create reusable accelerators (templates, libraries, patterns) to scale deployments across teams or customers.

Required Qualifications

• Programming & Scripting

o Languages:

§ UI Skills using React JS (Primary) If not the Angular

§ Python (primary for automation, APIs, data pipelines)

• API & Backend Engineering

o REST API development (Spring Boot / FastAPI / Node.js)

o API integration using:

§ OAuth2 / JWT authentication

§ API gateways (Azure API Management, Apigee)

o Data exchange formats: JSON, XML

§ HL7/FHIR (important in healthcare) - Secondary or nice to have

• AI/ML & GenAI Integration

o LLM integration:

§ Azure OpenAI / OpenAI APIs

o Frameworks: LangChain, Semantic Kernel

o RAG (Retrieval-Augmented Generation)

o Prompt engineering

o Embeddings + vector DBs (Pinecone, Azure Cognitive Search)

• Cloud & Infrastructure

o Azure (preferred in Eligible to workum ecosystem):

§ Azure App Services

§ Azure Functions (serverless)

§ Azure Kubernetes Service (AKS)

§ Azure Storage / Blob / Cosmos DB

o AWS (secondary):

§ Lambda, ECS/EKS, S3

• Data Engineering & Handling

o Any SQL RDBMS

o NoSQL - MongoDB preferred if not Cosmos DB

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