AI/ML & Forward Deployed Engineer

Job Description

Job Description

Job Title: Senior AI/ML Engineer Generative AI & Agentic AI
Experience: 8 Years
Location: Minnetonka Mills, MN
Employment Type: Contract
Rate: $55 to $60

Job Summary
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.

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.

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