Role: AI/ML & Forward Deployed Engineer
Location: Minnetonka Mills, MN - Remote
Rate - $75/hr
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.
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