AI/ML Engineering

Ampcus Inc. • Nashville, TN

1d

About The Position

Ampcus Inc. is seeking a strategic and technically strong leader to define and execute the organization’s AI/ML strategy and accelerate the adoption of artificial intelligence across products, engineering, and business functions. This role will lead the design, development, evaluation, and productionization of AI/ML solutions, with a strong focus on Generative AI, LLMs, AI agents, machine learning, intelligent automation, and AI-enabled software engineering. The ideal candidate combines deep technical expertise with strong business and organizational leadership skills and can translate emerging AI capabilities into scalable, secure, measurable business outcomes.

Requirements

• 10 years of experience in software engineering, machine learning, data science, AI engineering, or technology leadership.
• 5 years of hands-on experience with AI/ML technologies.
• Demonstrated experience taking AI/ML solutions from experimentation to production.
• Strong experience with Generative AI and Large Language Models (LLMs).
• Experience designing RAG and agentic AI architectures.
• Strong understanding of machine learning fundamentals and model lifecycle management.
• Experience with cloud AI/ML platforms such as AWS, Azure, or GCP.
• Experience with Python and modern AI/ML frameworks.
• Strong understanding of APIs, distributed systems, data architectures, and cloud-native technologies.
• Experience with AI/ML evaluation, monitoring, observability, and governance.
• Excellent communication and executive stakeholder-management skills.

Nice To Haves

• Large Language Models / Foundation Models
• Generative AI
• AI Agents / Agentic AI
• RAG / Retrieval Systems
• Prompt Engineering
• Embeddings & Vector Databases
• Model Fine-tuning
• LLM Evaluation
• Machine Learning & Deep Learning
• MLOps
• Python
• PyTorch / TensorFlow
• LangChain / LangGraph or equivalent frameworks
• Model APIs and AI platforms
• AWS / Azure / GCP
• Kubernetes / Docker
• APIs & Microservices
• Data Engineering & Data Pipelines
• AI Security & Responsible AI

Responsibilities

• Define and execute the enterprise AI/ML strategy aligned with business and technology objectives.
• Identify and prioritize high-value AI/ML use cases across products, engineering, and business operations.
• Develop AI/ML roadmaps covering experimentation, adoption, productionization, and scale.
• Evaluate emerging AI/ML technologies, models, platforms, and frameworks.
• Establish standards and best practices for AI/ML development, deployment, evaluation, and governance.
• Lead development and implementation of Generative AI solutions using LLMs and multimodal models.
• Design and implement RAG, agentic AI, tool use, function calling, and multi-agent architectures.
• Develop LLM-powered applications and intelligent workflows.
• Evaluate and select foundation models based on quality, latency, cost, security, and business requirements.
• Establish LLM evaluation frameworks covering accuracy, relevance, hallucination, safety, latency, and cost.
• Drive adoption of AI coding assistants and AI-powered software development practices.
• Identify opportunities to use AI to improve developer productivity, testing, quality, and engineering efficiency.
• Lead development and deployment of machine learning models across business and product use cases.
• Define ML modeling, experimentation, training, validation, and deployment strategies.
• Establish MLOps practices for model lifecycle management, monitoring, retraining, and governance.
• Partner with Data Science and Data Engineering teams to build reliable ML pipelines and data platforms.
• Apply statistical modeling, predictive analytics, classification, recommendation, anomaly detection, and optimization techniques where appropriate.
• Define scalable architectures for AI/ML applications and platforms.
• Design AI systems integrating models, data, APIs, enterprise applications, and business workflows.
• Establish patterns for model serving, inference, prompt management, vector databases, embeddings, and retrieval systems.
• Design for scalability, reliability, observability, performance, and cost optimization.
• Integrate AI/ML capabilities into existing enterprise technology ecosystems.
• Establish AI governance, security, privacy, and responsible AI practices.
• Partner with cybersecurity, legal, compliance, data governance, and architecture teams.
• Define controls for sensitive data, model access, prompt security, AI output validation, and model risks.
• Establish AI risk assessment and approval processes for production deployments.
• Ensure AI/ML solutions meet enterprise security, regulatory, and ethical requirements.
• Lead AI/ML initiatives from ideation → experimentation → MVP → production → scale.
• Establish processes for rapidly validating AI use cases while maintaining production engineering standards.
• Define KPIs and success metrics for AI initiatives.
• Measure business value, productivity improvements, quality improvements, cost savings, and adoption.
• Work closely with Product, Engineering, Data, UX, Security, and business stakeholders to deliver AI-powered products and capabilities.
• Drive organizational adoption of AI/ML technologies.
• Establish AI Centers of Excellence, communities of practice, or enablement programs.
• Educate engineering and business teams on effective AI adoption.
• Mentor AI/ML engineers, architects, data scientists, and technical leaders.
• Build and scale high-performing AI/ML engineering teams.
• Partner with executive leadership to communicate AI opportunities, risks, investments, and outcomes.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed