Alvarez & Marsal (A&M) is building trusted, governed AI capabilities that translate real consulting workflows into production-ready skills and agents. In this role, you will lead end-to-end delivery of applied AI solutions, partnering across business, Knowledge Management, Data, and engineering to design, build, deploy, monitor, and iterate systems that work in everyday client work.
Working onsite in Nashville, TN, the Manager, Applied AI Engineer role combines technical leadership with practical delivery ownership, guiding a pod through architecture choices, engineering standards, and mentoring while bringing clear outcomes to senior stakeholders.
What You’ll Do
- Own end-to-end delivery of applied AI solutions from proof of concept to production, including problem framing, building, deployment, monitoring, and iteration.
- Partner with business unit leaders to understand how they work, translate needs into skills and agents, and agree on clear success metrics.
- Collaborate with Knowledge Management and Data team leads to gather and clarify requirements and convert them into skill and agent designs.
- Work with Data Engineering, BIU, and Intelligence Layer teams to ensure solutions are built on reliable, production-grade data.
- Design, build, and operate headless skills and agentic workflows, including multi-step orchestration, tool routing, retrieval, guardrails, evaluation, and human-in-the-loop controls.
- Build and maintain automated skill-building pipelines, including templates, scaffolding, testing and evaluation harnesses, packaging, and deployment for consistent releases.
- Build integrations with large language model platforms and use the Model Context Protocol (MCP) to make skills available to approved AI tools and the Intelligence Layer.
- Integrate structured and unstructured enterprise content while preserving source permissions, metadata, and governance requirements.
- Guide the team in assessing current Power Platform and broader Microsoft stack processes, reimagining them as AI-first workflows rather than one-to-one rebuilds, and converting or connecting them to skills and agents.
- Train the wider team through workshops, playbooks, templates, worked examples, and office hours.
- Mentor engineers via pairing, code and design reviews, and hands-on coaching to raise quality across skills and agents.
- Apply and help shape engineering standards for skills and agents with the Intelligence Layer tech lead, including evaluation, CI/CD, versioning, reproducibility, and performance monitoring.
- Bring forward recommendations on tools, frameworks, and design patterns, with technical decisions made by the Intelligence Layer tech lead together with the Intelligence Layer lead.
- Support responsible AI use, including data privacy, client confidentiality, model risk, and decisions about third-party models.
- Communicate results, tradeoffs, risks, and blockers clearly to pod leadership, the Intelligence Layer tech lead, business unit leaders, and senior stakeholders.
Qualifications
- Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience.
- 5+ years of experience in software engineering, data science, machine learning, or applied AI engineering, including 2+ years building and shipping LLM or agent-based systems in production.
- Strong hands-on Python development experience.
- Experience building LLM and generative AI applications, including RAG, prompting, evaluation, and agentic patterns.
- Demonstrated track record designing, building, and shipping multiple AI agents and skills into production (examples or portfolio discussed at interview).
- Experience building automated pipelines for creating, testing, evaluating, and deploying skills or agents at scale.
- Hands-on experience with MCP, agent frameworks (for example, Claude Agent SDK, LangGraph, Semantic Kernel, or similar), and reusable skill design.
- Experience with CI/CD, testing, and observability for production systems.
- Excellent communicator who can explain technical concepts in plain business language and run discovery sessions with business unit leaders to draw out real requirements.
- Proven mentor and teacher who enjoys upskilling others, including non-technical colleagues.
- Comfort presenting to and facilitating workshops with senior stakeholders.
- Collaborative team player with strong cross-team relationship building.
- Bias toward action with high ownership, comfort with ambiguity, and sound judgment on when to ship, iterate, and stop.
Technologies
- Python, LLM, agent-based systems, RAG, Model Context Protocol (MCP)
- Claude Agent SDK, LangGraph, Semantic Kernel
- CI/CD, Power Platform, SharePoint, Power BI, Azure
- Power Automate, Power Apps, Fabric, SQL, Microsoft tools (SharePoint, Power Platform, Power BI)
Salary and Benefits
Salary: USD 140,000 - 180,000 per year.
- Healthcare plans
- Flexible spending and savings accounts
- Life, AD&D, and disability coverages
- 401(k) retirement savings plan
- Annual discretionary contribution to the 401(k) (eligibility requirements apply)
- Paid time off including vacation, personal days, seventy-two (72) hours of sick time (prorated for part time employees), ten federal holidays, and one floating holiday
- Parental leave
Team and Mission
The AI Lab Team sits within A&M’s Global AI and Knowledge Organization (GAIKO). GAIKO brings together the firm’s AI and Knowledge Management capabilities to deploy trusted, governed AI into everyday consulting work, with the Lab focused on turning repeatable business workflows into scalable agents and AI skills.
Nice to Have
- Experience with the Microsoft stack (SharePoint Online, Power Platform, Fabric/Power BI, Azure), including hands-on Power Automate and Power Apps experience and the ability to re-engineer existing flows (strongly preferred).
- Experience with SQL and modern data platforms.
- Experience working in Agile environments such as Scrum or Kanban.
- Experience in a professional services or consulting environment.
