Machine Learning Engineer (Pittsburgh, PA/Strongsville, OH/ Dallas TX)

• Strong hands-on experience in Machine Learning, Artificial Intelligence, and Generative AI development. \n

• Proven experience implementing AI/ML solutions in production environments. \n

• Hands-on experience working with Large Language Models (LLMs) and Generative AI applications. \n

• Experience developing agentic AI solutions and multi-step AI workflows. \n

• Strong programming skills in Python, including development of APIs, services, and ML/AI pipelines. \n, * Experience working with REST APIs, microservices, data pipelines, and enterprise integration patterns. \n

• Understanding of MLOps/LLMOps, CI/CD pipelines, automated testing, version control, deployment, and model monitoring. \n

• Experience with Git, Docker, Kubernetes, Jenkins, GitHub Actions, Azure DevOps, or comparable SDLC technologies. \n

• Knowledge of cloud-based AI/ML platforms such as Microsoft Azure/Azure AI, AWS/SageMaker/Bedrock, or Google Cloud/Vertex AI. \n

• Experience designing AI/ML solutions within model governance, model risk management, security, privacy, and regulatory frameworks. \n

• Strong analytical and troubleshooting skills for supporting models and AI applications in production. \n

• Strong communication and stakeholder-management skills, with the ability to work directly with internal clients to collect requirements and translate them into technical solutions.

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• Experience supporting release planning, implementation, production deployments, and post-release activities. \n

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Roles & Responsibilities:

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• Design, develop, orchestrate, and implement production-grade Machine Learning and Generative AI solutions. \n

• Build applications and services leveraging LLMs, Generative AI, Retrieval-Augmented Generation (RAG), and agentic AI frameworks. \n

• Develop and integrate AI agents and workflows using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent technologies. \n

• Evaluate and integrate foundation models and enterprise AI platforms based on business, security, performance, and governance requirements. \n

• Implement SDLC tooling and engineering harnesses to improve developer productivity, testing, deployment consistency, and overall team efficiency. \n

• Partner with internal clients and business stakeholders to elicit, analyze, and translate requirements into technical designs and implementation plans. \n

• Communicate technical requirements, solution designs, dependencies, and delivery expectations across engineering and business teams. \n

• Develop model pipelines covering data preparation, model integration, orchestration, testing, evaluation, deployment, monitoring, and ongoing support. \n

• Design solutions with model governance, responsible AI, risk management, security, auditability, and regulatory compliance incorporated throughout the development lifecycle. \n

• Support AI/ML models and services deployed within production applications, including troubleshooting, performance monitoring, and continuous improvement. \n

• Establish appropriate LL M evaluation and monitoring mechanisms, including model quality, hallucination risk, latency, reliability, and operational performance. \n

• Participate in release planning, release coordination, deployment activities, and hands-on production support during release windows. \n

• Collaborate with engineering, architecture, infrastructure, security, data, risk, and governance teams to ensure solutions meet enterprise standards. \n

• Maintain technical documentation covering solution architecture, model behavior, dependencies, deployment procedures, testing, and operational support. \n

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