About this opportunity
Compunnel, Inc. lists this Sr Machine Learning Engineer opportunity in northern, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
We are seeking a highly experienced Sr Machine Learning Engineer to design, develop, deploy, and scale enterprise-grade Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will possess deep expertise in AI platform engineering, cloud-native architectures, MLOps, and intelligent automation. This role requires hands‑on experience building production‑ready AI applications, Retrieval-Augmented Generation (RAG) solutions, AI agents, and large‑scale machine learning platforms while driving AI innovation, governance, and business transformation across the organization.
KEY RESPONSIBILITIES
Design, develop, test, deploy, and maintain Machine Learning, Generative AI, and Agentic AI solutions in production environments.
Collaborate with Data Scientists, Software Engineers, Architects, and DevOps teams to deliver scalable AI products and enterprise platforms.
Build and operationalize Large Language Model (LLM) applications using foundation models and enterprise AI services.
Design and implement Retrieval-Augmented Generation (RAG) architectures integrating enterprise knowledge repositories, vector databases, and semantic search capabilities.
Develop AI-powered applications utilizing advanced prompt engineering, context management, and reasoning techniques.
Build and orchestrate AI agents and multi-agent systems capable of autonomous reasoning, planning, workflow execution, and decision support.
Establish prompt engineering frameworks, evaluation methodologies, and optimization processes to improve AI application performance and reliability.
Translate business requirements into scalable AI-driven solutions that deliver measurable business value.
Design, build, deploy, and maintain AI/ML and Generative AI platforms on AWS and Databricks.
Data Ingestion
Data Preparation
Implement and maintain MLOps and LLMOps frameworks for enterprise-scale AI lifecycle management.
Develop CI/CD automation processes supporting AI application delivery and model deployment.
Build AI observability and monitoring solutions to track:
Data Drift
Hallucinations
Cost Optimization
Business Outcomes
Ensure production AI systems meet requirements for reliability, scalability, performance, security, and compliance.
Evaluate emerging AI technologies, frameworks, platforms, and foundation models for enterprise adoption.
AGENTIC AI & INTELLIGENT AUTOMATION
Design and implement agentic AI workflows integrated with enterprise systems, APIs, databases, and knowledge repositories.
Develop intelligent automation solutions that increase operational efficiency and reduce manual effort.
Build human-in-the-loop review mechanisms and governance workflows for AI-assisted decision making.
Develop tool-using AI agents capable of securely interacting with enterprise applications, APIs, and external services.
Implement agent orchestration patterns to support complex business workflows and decision automation.
AI GOVERNANCE & RESPONSIBLE AI
Develop and maintain documentation, standards, policies, and governance frameworks for AI and Machine Learning solutions.
Ensure compliance with Responsible AI principles, including:
Transparency
Explainability
Privacy
Security
Regulatory Compliance
Partner with Risk, Security, Legal, and Governance teams to establish enterprise AI controls and monitoring capabilities.
Support model validation, explainability, auditability, and compliance requirements.
Implement governance controls for AI lifecycle management and operational oversight.
LEADERSHIP & STRATEGY
Serve as a technical leader and mentor to AI Engineers, Data Scientists, and Software Engineering teams.
Contribute to enterprise AI strategy, architecture standards, and technology roadmaps.
Identify opportunities to leverage AI, Generative AI, and Intelligent Automation to create business value.
Communicate complex AI concepts, risks, and recommendations to both technical and non-technical stakeholders.
Promote AI best practices, engineering excellence, and continuous innovation across the organization.
Drive adoption of emerging AI technologies and modern engineering methodologies.
REQUIRED QUALIFICATIONS
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
Minimum 8 years of experience in: Machine Learning Engineering
MLOps
Software Engineering
Related Technical Disciplines
Minimum 3 years of hands‑on experience deploying AI/ML solutions in cloud environments.
Generative AI Solutions
Large Language Model (LLM) Applications
Retrieval-Augmented Generation (RAG) Systems
Agent-Based Solutions
Strong hands‑on experience with AWS AI and cloud services, including: Amazon SageMaker
Amazon Bedrock
AWS Lambda
AWS Step Functions
AWS CloudFormation
Amazon ECS
Amazon EKS
Strong experience building and deploying AI applications in production environments.
Expertise with AI development frameworks and orchestration platforms, including: LangChain
LangGraph
Semantic Kernel
CrewAI
AutoGen
Experience designing and implementing RAG architectures and vector database solutions.
Experience building AI agents, multi-agent systems, and intelligent automation workflows.
Advanced Python programming skills and experience with AI/ML libraries and frameworks.
Experience with: Docker, Containerized Deployments, Cloud‑Native Architectures, MLOps Frameworks, LLMOps Platforms, Model Monitoring Solutions, AI Observability Practices
Strong understanding of Software Engineering and DevSecOps best practices.
Experience architecting scalable, resilient, and secure AI platforms.
PREFERRED QUALIFICATIONS
Experience with Databricks-based AI and Machine Learning platforms.
Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or similar technologies.
Familiarity with enterprise knowledge management and semantic search platforms.
Experience implementing advanced AI governance and Responsible AI frameworks.
Experience building enterprise intelligent automation and decision intelligence solutions.
Knowledge of model evaluation frameworks and AI benchmarking methodologies.
Experience working in regulated industries requiring strict governance and compliance standards.
Experience supporting enterprise AI transformation initiatives.
CERTIFICATIONS
AWS Certified Solutions Architect – Associate or Professional (Preferred)
Kubernetes Certifications (CKA / CKAD) (Preferred)
Generative AI, MLOps, or AI Engineering Certifications (Preferred)
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.