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# AI / ML Engineer

- **Company:** INSPIRE GLOBAL SOLUTIONS LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $145,600.0 - $166,400.0
- **Contract:** Contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Optical Character Recognition (OCR), Computer Vision, User Authentication, Automation of Tests, Microsoft Azure, Cloud Storage, Databases, Continuous Integration, Couchbase Servers, Software Debugging, Python (Programming Language), Machine Learning, Oracle (Applications), Recommender Systems, Redis, OpenAI, Cloud Services, Standard Sql, Azure Machine Learning, Software Safety, Systems Integration, Management of Software Versions, Enterprise Search, Retrieval-Augmented Generation, Large Language Models, Snowflake, Git, Kubernetes, Azure AKS, Machine Learning Operations, Model Registry, Restful APIs, Model Context Protocol, Terraform, Data Pipelines, Dynatrace, Servicenow, Databricks
- **Published:** October 30, 2026
- **Apply:** https://www.dice.com/job-detail/8b1c62fd-85b9-4054-8358-8bd06a01972e

## About the Role

The role requires demonstrated production experience in both generative AI and conventional machine learning, strong troubleshooting judgment and the ability to prioritize competing demands. Work closely with application, data, cloud and model owners to resolve incidents and deliver sustainable operational improvements., * Demonstrated production support or engineering experience across both generative AI services and machine learning pipelines, including incident response, deployments, monitoring and recovery. Be able to explain direct contributions and operational outcomes in each area.

 * Hands-on enterprise LLM API integration and gateway troubleshooting, plus practical RAG, embeddings, vector or hybrid search, retrieval filtering and response evaluation experience.
 * Hands-on Databricks or comparable ML-platform experience, including job diagnostics, model/artifact versioning, reproducibility, batch or online inference, evaluation and rollback.
 * Strong Python and SQL skills for debugging, data investigation and automation. Experience diagnosing REST APIs, authentication and service-to-service failures.
 * Experience with Git, CI/CD, automated testing and controlled releases. Working knowledge of containers, Kubernetes application diagnostics and cloud service dependencies.
 * Ability to select useful monitoring and evaluation measures, distinguish data problems from model regressions, and communicate business impact and technical findings clearly.
 * Ability to prioritize a shared incident and improvement backlog, document operational procedures and train backup resources. A relevant degree or equivalent professional experience., Azure Foundry/Azure OpenAI, Azure AI Search, Databricks and Snowflake production experience are strongly preferred. Additional useful experience includes MLflow or another model registry, Redis, Couchbase, Oracle, Azure Blob, AKS, App Service, Azure DevOps, Terraform, distributed tracing, Model Context Protocol integrations, recommendation systems, computer vision and OCR. ServiceNow and ITIL-aligned incident, problem and change practices are beneficial. Certifications supplement demonstrated production capability.

## Description

Serve as the primary technical resource for production generative AI and machine learning operations. This single individual-contributor position combines LLM gateway and retrieval support with model deployment, inference, evaluation and pipeline operations. Restore service, maintain output quality, improve monitoring and release controls, and automate recurring operational tasks across both areas., * Provide Level 2/Level 3 support through ServiceNow. Assess business impact, diagnose incidents, coordinate recovery and escalation, and document root causes, workarounds and corrective actions.

 * Troubleshoot LLM gateways and provider integrations, including authentication, quotas, rate limits, routing, timeouts, retries and approved fallback behavior. Trace failures across APIs and downstream services.
 * Operate retrieval-augmented generation (RAG) and search workflows. Investigate indexing failures, stale knowledge, embeddings, filtering and relevance issues in Azure AI Search and connected data pipelines.
 * Support Databricks model, processing and evaluation jobs and batch or online inference. Investigate runtime failures, dependencies, schema changes, data freshness, scoring errors and degraded predictions with data and model owners.
 * Maintain evaluation datasets and automated checks for retrieval relevance, groundedness, response safety and model quality. Monitor drift and regressions using suitable baselines and business acceptance criteria; coordinate retraining or model changes with accountable owners.
 * Monitor availability, latency, errors, throughput, token usage/cost, job success and output freshness. Correlate logs, metrics and traces to distinguish data, model, provider, application and infrastructure failures.
 * Version code, prompts, configurations and model artifacts. Maintain reproducible environments, automated tests and controlled release, promotion and rollback procedures using Git and CI/CD.
 * Support vision, image-processing or OCR workflows when assigned. Diagnose input and output-quality issues and engage domain specialists for deeper model or algorithm changes.
 * Automate repeatable checks and recovery tasks. Maintain runbooks, dependency maps, handovers and escalation paths; apply approved access controls, secret handling, sensitive-data protection and AI safety practices., Own diagnosis and operational coordination for assigned AI/ML services. Partner with application engineers on APIs and code defects, data engineers on data pipelines and stores, and cloud engineers on infrastructure. Model and business owners define acceptance criteria and approve material behavior changes. Deep database/cluster administration, new-model research and major feature development remain with the accountable specialist teams.