Sr. Machine Learning Engineer

Sabio infotech; Cincinnati (OH); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Sabio infotech in Cincinnati, OH is seeking a Sr. Machine Learning Engineer to design, deploy, and maintain scalable ML and Generative AI solutions with emphasis on reliability, performance, security, and business value.

The role leads automation-first AI engineering, builds end-to-end ML models, applies Responsible AI, and ensures governance, risk management, and production readiness through MLOps, CI/CD, and monitoring within regulated environments.

Qualifications

• Extensive experience designing, developing, and deploying ML solutions in production.
• Hands-on Generative AI with LLMs, RAG, vector databases, and modern AI frameworks.
• Strong ML model development, feature engineering, evaluation, and monitoring.
• Experience with governance, compliance, and validation in regulated environments.
• MLOps: deployment, versioning, monitoring, retraining, and CI/CD pipelines.
• Strong Responsible AI, explainability, governance, and risk management concepts.
• Proficiency in Python and ML ecosystems (TensorFlow, PyTorch, Scikit-learn, LangChain).
• Excellent communication, problem solving, and stakeholder management.

Responsibilities

• Design, develop, deploy, and maintain scalable ML and Generative AI solutions focusing on reliability, performance, security, and business value.
• Champion automation-first AI engineering, improving operational efficiency and reducing manual work.
• Build and operationalize ML models and AI-enabled apps across the lifecycle, from experimentation to production.
• Develop and deploy Generative AI apps in production, especially in regulated industries.
• Apply Responsible AI principles, ensuring fairness, explainability, privacy, and security.
• Perform model risk evaluations and governance documentation; address identified risks.
• Establish MLOps frameworks for lifecycle management, monitoring, and auditability.
• Collaborate with Risk, Compliance, InfoSec, and business partners to meet standards.
• Implement CI/CD, automated testing, model monitoring, observability, and production support.
• Evaluate emerging ML/AI technologies and recommend adoption strategies.
• Mentor team on best practices in ML engineering, MLOps, and production AI.

Skills

Production ML design

Generative AI development

MLOps practices

Python programming

Model monitoring

Tools

TensorFlow

PyTorch

Scikit-learn

LangChain

Semantic Kernel

Job description

Sr. Machince Learning Engineer

Location: Cincinnati OH (Hybrid - 3 Days Onsite)

Duration: 1 Year

Key Responsibilities

• Design, develop, deploy, and maintain scalable machine learning and Generative AI solutions with a focus on reliability, performance, security, and business value.
• Champion an automation-first approach to software and AI engineering, identifying opportunities to improve operational efficiency and reduce manual processes.
• Build and operationalize machine learning models and AI-enabled applications throughout the entire model lifecycle, from experimentation to production deployment and monitoring.
• Develop and deploy Generative AI applications in production environments, preferably within financial services or other highly regulated industries.
• Apply and advocate Responsible AI principles, ensuring solutions meet requirements for fairness, explainability, transparency, privacy, security, and compliance.
• Perform model risk evaluations, complete required governance documentation and questionnaires, and partner with stakeholders to address and remediate identified risks.
• Establish and maintain frameworks for MLOps, model lifecycle management, monitoring, validation, version control, auditability, and AI governance.
• Collaborate with Risk, Compliance, Information Security, and business partners to ensure machine learning solutions meet enterprise and regulatory standards.
• Implement CI/CD pipelines, automated testing, model monitoring, observability, and production support processes for machine learning applications.
• Evaluate emerging machine learning and AI technologies and recommend appropriate adoption strategies.
• Mentor team members on best practices in machine learning engineering, MLOps, Responsible AI, and production AI systems.

Required Qualifications

• Extensive experience designing, developing, and deploying machine learning solutions in production environments.
• Hands-on experience developing and deploying Generative AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and modern AI development frameworks.
• Strong understanding of machine learning model development, feature engineering, model evaluation, performance optimization, and model monitoring.
• Experience conducting model risk assessments and supporting governance, compliance, and validation requirements within regulated environments.
• Practical experience implementing MLOps practices including model deployment, versioning, monitoring, automated retraining, and CI/CD pipelines.
• Strong understanding of Responsible AI, model explainability, governance, and risk management concepts.
• Proficiency in Python and modern machine learning ecosystems, including frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, Semantic Kernel, or equivalent technologies.
• Strong communication, problem-solving, and stakeholder management skills.