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About This Role
Nashville, TennesseeJob Description
The AI Engineer is responsible for designing, developing, testing, and deploying AI-enabled solutions that support the Firm’s legal and business operations. Working directly with attorneys, practice groups, and Firmwide Department teams, this role translates business needs and workflows into practical, scalable AI solutions using technologies such as Azure Databricks, Microsoft Azure, n8n, LangGraph, and Databricks Agent Bricks. As part of the Firm’s AI engineering team, the AI Engineer will contribute throughout the development lifecycle, from discovery and prototyping through production deployment, monitoring, and continuous improvement. The role will collaborate closely with Senior AI Engineers, Data Architects, DevOps teams, and other technical stakeholders while applying shared engineering standards and best practices. Engineers may bring complementary strengths in areas such as data engineering, application and front-end development, or applied data science, with assignments aligned to individual capabilities, development interests, and evolving business needs.
KEY RESPONSIBILITIES
- Partner with attorneys, practice groups, and business teams to identify AI opportunities, understand workflows, define requirements, and establish measurable success criteria.
- Design, develop, test, and deploy AI agents and multi-step workflows using platforms and frameworks such as Azure Databricks, n8n, LangGraph, and Databricks Agent Bricks.
- Integrate AI solutions with enterprise applications, APIs, governed data sources, and native Microsoft Azure services, incorporating appropriate human review and approval processes.
- Develop prototypes in collaboration with users and translate successful concepts into reliable, scalable production applications.
- Build and maintain evaluation datasets, automated tests, and quality measures to assess agent behavior, retrieval quality, tool-use accuracy, task completion, performance, latency, and cost.
- Analyze execution traces, system behavior, and failure patterns to troubleshoot issues and continuously improve AI solutions.
- Collaborate with Data Architecture and DevOps teams to support governed data access, application deployment, environment configuration, monitoring, and production support.
- Design AI solutions with appropriate reliability safeguards, including error handling, retries, timeouts, stopping conditions, recovery processes, and controls designed to prevent unintended or duplicate actions.
- Apply security, access controls, data protections, source traceability, and responsible AI practices throughout solution design and deployment, including protections against prompt injection, inappropriate access, and data leakage.
- Engage with users following deployment to gather feedback, support adoption, measure business impact, and identify opportunities for continued enhancement.
- Participate in code reviews, technical documentation, knowledge sharing, and the development of reusable components, standards, and engineering best practices.
REQUIRED EDUCATION, KNOWLEDGE & EXPERIENCE
- Strong Python programming skills and demonstrated ability to develop maintainable, production-quality software using APIs, automated testing, version control, and code review.
- Working knowledge of SQL and experience integrating application, data, and AI components.
- Hands-on experience developing AI agents, agent-enabled applications, or multi-step AI workflows involving language models, enterprise information, APIs, or external tools.
- Experience with prompt and context design, structured outputs, tool integration, state management, error handling, and human-in-the-loop workflows.
- Practical experience with Azure Databricks and working knowledge of Microsoft Azure services used for application development, integration, or deployment.
- Understanding of retrieval-augmented generation (RAG), embeddings, retrieval techniques, and the relationship between source quality and AI system performance.
- Experience evaluating and troubleshooting AI applications, including analyzing execution traces, failures, task completion, quality, latency, or cost.
- Understanding of production engineering practices, including application deployment, configuration management, secrets management, monitoring, automated testing, and troubleshooting.
- Working knowledge of authentication, authorization, role-based access controls, and security considerations associated with enterprise AI applications and sensitive information.
- Strong analytical and problem-solving skills with the ability to translate ambiguous business requirements into practical technical solutions.
- Ability to communicate technical concepts and tradeoffs effectively to both technical and non-technical stakeholders.
- Demonstrated collaboration, accountability, curiosity, and ability to work effectively with engineers, domain experts, and business users.
PREFERRED SKILLS & KNOWLEDGE
- Experience deploying, monitoring, and supporting AI applications in a production environment.
- Experience with one or more AI orchestration or workflow technologies, including n8n, LangGraph, Databricks Agent Bricks, or comparable platforms.
- Experience with MLflow, LangSmith, or similar AI tracing, observability, and evaluation tools.
- Advanced experience with Azure Databricks technologies, including Spark, Delta Lake, Unity Catalog, Lakebase, or Delta Sharing.
- Experience designing and optimizing RAG solutions, including vector search, hybrid retrieval, filtering, reranking, document processing, metadata extraction, and retrieval evaluation.
- Experience with graph databases, knowledge graphs, graph-based RAG, or techniques for identifying and linking entities and relationships across information sources.
- Experience working with PostgreSQL or similar technologies to support persistent agent state, checkpoints, or memory.
- Front-end or application development experience using TypeScript, JavaScript, React, or similar technologies.
- Experience designing user-facing AI applications incorporating streaming responses, citations, progress indicators, feedback mechanisms, and human review.
- Experience developing reusable AI tools and enterprise integrations, including Model Context Protocol (MCP) or comparable integration approaches.
- Experience designing evaluation datasets, benchmarks, regression testing, automated scoring, or LLM-based evaluation methods.
- Knowledge of statistical analysis or experimental design used to evaluate AI system performance and improvements.
- Experience with Microsoft Entra ID, managed identities, and enterprise authentication and authorization models.
- Experience working in legal services, professional services, technical consulting, or another environment involving sensitive information and complex business 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 $85,000 - $170,000. 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 $85K-$170K range is in the lower quartile 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($127K) sits 40% below the category median. Disclosed range: $85K to $170K.
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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