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Machine Learning Tech Lead

LinkedIn Crowe Nashville, TN
Not Applicable Posted March 13, 2026 Job link
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Requirements
  • Programming experience.
  • You’re experienced writing scalable, production-level code in Python.
  • You’re familiar with Linux/UNIX systems.
  • Machine learning experience.
  • You’re proficient in machine learning packages, such as Tensorflow and Pytorch, and have proven expertise in designing/developing AI/ML models.
  • You understand the mechanics of supporting ML solutions in a production environment and can help develop/maintain tests that validate functionality and evaluate model performance.
  • GenAI Experience.
  • Experience designing and implementing AI agents using large language models (LLMs), including prompt engineering, tool use, memory, and multi-agent orchestration patterns.
  • Proficiency with modern AI/ML frameworks, tools, and agent platforms (e.g.
  • MCP, A2A, Microsoft Foundry, OpenAI Agent SDK, Semantic Kernel, LangChain, LlamaIndex, Microsoft Copilot Studio, or equivalent).
  • Knowledge of retrieval-augmented generation (RAG), vector databases, embeddings, and techniques for grounding model outputs in enterprise data.
  • Software Experience.
  • You’re familiar with the software development lifecycle, and ideally tenets of MLOps.
  • You have exposure to CI/CD frameworks and tools like Docker and Git.
  • You’re experienced deploying code in production environments.
  • AI-Assisted Software Engineering: Demonstrated ability to embrace and advocate for AI-assisted software engineering practices, including the effective use of coding assistants (e.g.
  • Claude Code), automated testing, and design-time AI tools to accelerate solution design, improve quality, and enhance developer productivity.
  • Communication.
  • Excellent communication skills, capable of effectively documenting and summarizing technical details for non-technical stakeholders.
  • Agile experience.
  • You’re experienced attending Scrum or Kanban meetings; you favor incremental, iterative improvements through regular releases, testing, and monitoring.
Preferred Skills
  • AI-Assisted Software Engineering: Demonstrated ability to embrace and advocate for AI-assisted software engineering practices, including the effective use of coding assistants (e.g.
  • You’re experienced attending Scrum or Kanban meetings; you favor incremental, iterative improvements through regular releases, testing, and monitoring.
  • AI Agent Experience.
  • Familiarity with evaluation, monitoring, and observability of AI agents, including guardrails, hallucination mitigation, and human-in-the-loop designs.
  • Ability to translate business requirements into agent behaviors, decision logic, and measurable performance outcomes.
  • Proven ability to design end-to-end AI architectures that integrate agents with enterprise systems (ERP, CRM, data platforms, APIs) using scalable, secure integration patterns.
  • Cloud Experience: Experience designing and deploying AI/ML solutions on major cloud platforms (AWS, Azure, and/or GCP), including the use of managed AI services, cloud-native integration patterns, scalable data architectures, and secure, enterprise-grade deployments.
  • Knowledge sharing.
  • You enjoy sharing what you learn, whether by offering cross-training opportunities, giving internal team “lightning talks,” or by writing detailed comments on tickets when you close them.
  • Curiosity about AI and machine learning.
  • You want to stay fresh in AI and machine learning.
  • You demonstrate curiosity and continuous learning mindset in Generative AI and applied machine learning, with an interest in staying current on emerging models, architectures, and techniques, including engaging with original research, technical papers, and pre-release concepts before they are broadly commercialized
  • Professional Services experience.
  • You’re familiar with the landscape of a professional services firm and interested in engaging with the unique value proposition of products associated with a diverse range of services, from advising and consulting to tax and public accounting.
Education
  • (Not required) – Cloud Experience: Experience designing and deploying AI/ML solutions on major cloud platforms (AWS, Azure, and/or GCP), including the use of managed AI services, cloud-native integration patterns, scalable data architectures, and secure, enterprise-grade deployments.
  • (Not required) – Knowledge sharing.
  • (Not required) – Curiosity about AI and machine learning.
  • (Not required) – You want to stay fresh in AI and machine learning.