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About This Role
Nashville, TennesseeJob Description
The Senior AI Engineer is responsible for designing, developing, deploying, and continuously improving production AI agents and agentic workflows that support attorneys, practice groups, and Firmwide Department teams across the Firm. This role translates complex business needs into reliable AI enabled solutions that combine language models, enterprise tools, governed data, workflow automation, and appropriate human review. Working primarily within Azure Databricks and/or Azure AI Studio with native Azure services, the Senior AI Engineer will develop solutions using technologies such as n8n, LangGraph, Python, and enterprise APIs. The position will maintain a strong hands-on development focus while also providing technical guidance, code review, mentoring, and training to AI Engineers and helping establish engineering standards and best practices. The Senior AI Engineer will partner closely with the department leadership, AI Engineers, Data Architects, DevOps teams, and business stakeholders to deliver secure, scalable, and production ready AI solutions.
KEY RESPONSIBILITIES
- Partner with attorneys, practice groups, and Firmwide teams to understand existing workflows, identify opportunities for AI-enabled solutions, and define technical requirements, acceptance criteria, and appropriate boundaries for automation and human review.
- Design, build, deploy, and maintain production AI agents and agentic workflows using n8n, LangGraph, Python, enterprise APIs, and related technologies.
- Develop n8n workflows incorporating triggers, webhooks, AI Agent nodes, enterprise connectors, reusable sub-workflows, custom code, secure credential management, and appropriate error handling.
- Build stateful LangGraph workflows incorporating tool calling, conditional routing, checkpoints, persistent state, stopping conditions, recovery mechanisms, and human approval steps.
- Design prompts, context assembly, structured outputs, retrieval-augmented generation (RAG), and model selection strategies based on defined quality, latency, cost, and business requirements.
- Develop reusable agent tools, APIs, and integrations that enable agents to retrieve information and perform authorized actions securely across enterprise systems.
- Partner with the Data Architect to implement governed and permission-aware data access, retrieval, persistent state, and memory while maintaining source traceability and appropriate user and matter-level access controls.
- Develop evaluation datasets, automated tests, and regression testing processes in partnership with domain experts to assess agent behavior, retrieval quality, tool-use accuracy, task completion, groundedness, latency, and cost.
- Analyze agent traces, tool calls, workflow outcomes, and failure patterns using evaluation and observability tools to troubleshoot issues and continuously improve solution performance.
- Incorporate appropriate security and reliability controls, including input and output validation, authorization, credential protection, retries, timeouts, stopping conditions, recovery procedures, and escalation paths.
- Test solutions for risks such as prompt injection, inappropriate tool use, unauthorized data access, and data leakage, incorporating human approval for consequential actions where appropriate.
- Partner with DevOps teams to support application deployment, environment configuration, secrets management, monitoring, release management, observability, and rollback processes across Azure environments.
- Maintain ownership of the production performance, troubleshooting, maintenance, and continuous improvement of assigned AI agents and workflows.
- Mentor and train AI Engineers through technical guidance, pairing, code reviews, and practical learning sessions.
- Establish, document, and promote engineering best practices related to agent architecture, workflow development, evaluation, testing, security, observability, and production delivery.
REQUIRED EDUCATION, KNOWLEDGE & EXPERIENCE
- Demonstrated experience personally designing, developing, deploying, and supporting production AI agents, agentic applications, or tool-enabled large language model (LLM) workflows.
- Strong Python development skills and working knowledge of SQL, APIs, automated testing, version control, code review, and maintainable software integration practices.
- Hands-on experience with LangGraph or a comparable framework used to develop stateful, multi-step AI agents.
- Experience with n8n or a comparable workflow automation platform and the ability to develop and support solutions that combine workflow automation with custom agent services.
- Hands-on experience delivering AI solutions using Azure Databricks, Azure AI Studio, and/or native Microsoft Azure services, including model access, application deployment, and integration with governed enterprise data.
- Strong understanding of agent architecture and workflow design, including tool schemas, prompt and context management, structured outputs, state transitions, memory, checkpointing, stopping conditions, and human review.
- Experience designing integrations using APIs, webhooks, and asynchronous execution, including authentication, rate limits, retries, error handling, and idempotency.
- Experience implementing retrieval-augmented generation (RAG) and governed data access, including retrieval filtering, source permissions, citations, and validation of retrieved information.
- Experience developing evaluation datasets, automated tests, and regression testing processes and using agent traces and failure analysis to improve solution performance.
- Understanding of AI application security and reliability practices, including authorization, credential protection, sensitive information handling, constrained tool access, recoverable workflows, and appropriate human controls.
- Experience with production software delivery practices, including versioning, environment configuration, automated testing, observability, monitoring, and rollback procedures.
- Demonstrated ability to translate ambiguous or complex business requirements into practical technical solutions and clearly communicate technical considerations and tradeoffs to technical and non-technical stakeholders.
- Demonstrated ability to mentor and train other engineers, conduct effective code reviews, and contribute to technical standards and best practices while maintaining significant hands-on development responsibilities.
PREFERRED SKILLS & KNOWLEDGE
- Production experience using both n8n and LangGraph, particularly solutions combining workflow automation with custom Python-based agent services.
- Experience deploying agents through Azure Databricks and using Lakebase or similar technologies for persistent agent state, checkpoints, and memory.
- Experience with AI evaluation and observability tools such as MLflow, LangSmith, or comparable platforms.
- Experience developing Model Context Protocol (MCP) servers, reusable agent tools, or integrations connecting AI solutions with enterprise systems and governed data.
- Experience with multi-agent architectures, model routing, advanced retrieval techniques, and other agentic design patterns.
- Working knowledge of JavaScript or TypeScript for custom integrations and lightweight application interfaces.
- Experience developing user interfaces that provide visibility into agent progress, source citations, approval workflows, and user feedback.
- Experience delivering AI or technology solutions directly with business users in legal services, professional services, or other environments involving sensitive information and complex workflows.
PHYSICAL REQUIREMENTS
- Ability to sit and stand for extended periods.
- Ability to lift up to 15 pounds.
The expected salary range for this position is $170,000 - $200,000. Final compensation will be determined based on several factors, including but not limited to, relevant experience, qualifications, skill set, and geographic location.
Pillsbury Winthrop Shaw Pittman LLP is an Equal Opportunity Employer.
If you require an accommodation in order to apply for a position, please contact us at [email protected].
Salary Context
This $170K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Pillsbury Winthrop Shaw Pittman, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $170K to $200K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Pillsbury Winthrop Shaw Pittman AI Hiring
Pillsbury Winthrop Shaw Pittman has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Nashville, TN, US. Compensation range: $170K - $200K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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