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About This Role
Dechert LLP is a global specialist law firm focused on highend legal work. The AI Engineer is embedded within the Dechert Innovation Laband works directly with attorneys, legal professionals, and business\-servicesteams to discover, evaluate, prototype, and pilot emerging AI\-enabled technologiesthat may improve the delivery of legal services and firm operations. This roleoperates at the intersection of technology scouting, rapid experimentation,artificial intelligence, automation, and enterprise innovation.
The role translates complex legal and operational needs into testableprototypes and proof\-of\-concept solutions—including generative AI applications,retrieval\-augmented generation (RAG) solutions, workflow automations,intelligent agents, integrations, and custom web applications—to assessfeasibility, value, and readiness for broader adoption.
The position is part of the Dechert Innovation Lab’s rapid\-experimentationfunction and is responsible for moving high\-value opportunities from ideathrough proof of concept and pilot, evaluating outcomes, and providingrecommendations on whether and how to transition successful experiments to theappropriate enterprise applications, platform, or operations team for productiondeployment and long\-term support.
ESSENTIAL JOB FUNCTIONS:
- Partner with attorneys, practice groups, legal project management, knowledge management, finance, risk, client development, and business\-services teams to identify innovation opportunities, understand workflows, pain points, and desired outcomes.
- Facilitate technical discovery and innovation sessions; assess business problems for AI, automation and emerging technologies.
- Rapidly design, develop, and evaluate proof\-of\-concept and pilot solutions using approved and emerging AI platforms, APIs, low\-code tools, workflow automation platforms, and custom development technologies.
- Build and test experimental AI\-enabled applications, including generative AI assistants, document and knowledge\-search solutions, RAG applications, workflow copilots, intelligent agents, and decision\-support tools.
- Evaluate and select appropriate technical approaches based on innovation potential, business value, solution complexity, data sensitivity, scalability, supportability, and time\-to\-value.
- Experiment with solution success measures, such as feasibility, time saved, adoption potential, accuracy, user satisfaction, process\-cycle reduction, risk reduction, and business impact.
- Evaluate pilot outcomes and recommend whether to transition successful experiments to the appropriate enterprise application teams.
- Relentlessly scout and stay ahead of the curve on AI engineering practices, legal\-industry AI use cases, emerging tools and platforms, AI governance requirements, and applicable technology trends; recommend new technologies for future evaluation.
- Participate in intake prioritization, solution estimation, innovation pipeline planning, vendor evaluations, and portfolio reporting.
- Other responsibilities as needed.
KNOWLEDGE:
- Experience with Generative AI, Model Context Protocol (MCP), Azure/OpenAI, Large Language Model (LLM), Claude, Microsoft CoPilot, prompt engineering, retrieval\-augmented generation, embeddings, vector databases, AI agents, model evaluation, and responsible AI practices.
- Application development concepts, including APIs, microservices, web applications, databases, authentication, authorization, logging, monitoring, testing, and CI/CD practices.
- Automation and orchestration technologies, such as workflow platforms, robotic process automation, low\-code/no\-code development tools, and integration platforms.
- Software development methodologies, including Agile, Kanban, rapid prototyping, product discovery, and iterative delivery.
SKILLS:
- Strong software engineering skills in one or more modern programming languages, such as Python, JavaScript/TypeScript, C\#, ASP.NET Core, SQL, or similar technologies.
- Ability to build and deploy AI\-enabled applications using APIs, SDKs, orchestration frameworks, cloud services, and enterprise platforms.
- Ability to translate ambiguous business needs into testable hypotheses and experiment designs.
- Strong consultative and communication skills, with the ability to work effectively with attorneys, business leaders, technical teams, vendors, and nontechnical users.
- Strong analytical, problem\-solving, and systems\-thinking abilities.
- Ability to balance speed, experimentation, and learning with security, quality, governance, maintainability, and long\-term supportability.
- Experience designing user\-centered solutions and incorporating feedback into rapid experiment cycles and iterations.
- Comfort with ambiguity in a fast\-moving environment with multiple concurrent initiatives.
- Resilience and persistence in iterative experimentation, including the ability to learn from unsuccessful outcomes, adapt approaches, and maintain momentum through successive testing cycles.
INTERESTS:
- Genuine curiosity about exploring emerging technologies and applying AI to meaningful legal, client\-service, and business\-operations challenges.
- Interest in working directly with end users to test solutions.
- Interest in responsible AI, data protection, human\-centered design, and practical technology governance.
- Enthusiasm for hands\-on experimentation, continuous learning, and evaluating emerging technologies.
EDUCATION AND EXPERIENCE:
- Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Science, Artificial Intelligence, or a related technical discipline required; equivalent combination of education, training, and relevant experience may be considered.
- Minimum of 5 years of experience in software engineering, application development, automation, systems integration, data engineering, rapid prototyping, or related technical roles.
- Minimum of 2 years of experience designing, developing, prototyping, or piloting AI\-enabled, machine\-learning, generative AI, automation, or intelligent workflow solutions preferred.
- Experience building applications using large language model APIs, RAG architectures, AI orchestration frameworks, Model Context Protocol (MCP), Claude,
- Azure/OpenAI, vector search technologies, or agentic workflow patterns strongly preferred.
- Experience with cloud platforms such as Microsoft Azure or Amazon Web Services.
- Experience with enterprise integrations, APIs, identity and access management, secure development practices, and application lifecycle management strongly preferred.
- Experience in a law firm, legal technology provider, consulting firm, financial\-services organization, or other regulated professional\-services environment is preferred but not required.
- Experience working with cross\-functional stakeholders and delivering technology solutions from discovery and experimentation through pilot and production deployment required.
- Relevant certifications in cloud engineering, AI, software development, security, automation, Agile delivery, or legal technology are preferred but not required.
Additional Job Description
At the time of this posting, the salary range for this position in Boston, New York and Washington, D.C. is between $140,000\.00 – $175,000\.00 annually. Actual compensation is commensurate with job related knowledge, skills, experience, and location of the position.
Location(s)
Boston, Philadelphia, New York and Washington, D.C.
Time Type
Full time
Dechert LLP is committed to ensuring equal employment opportunity and non\-discrimination. The Firm prohibits unlawful discrimination in any term or condition of employment against any employee or applicant for employment because of the individual’s race, color, creed, religion, sex, age, marital status, national origin, ancestry, citizenship, sexual orientation, gender identity or expression, genetic information, disability, membership or service in the armed forces, or any other characteristic protected by law.
Salary Context
This $140K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Dechert LLP, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($157K) sits 27% below the category median. Disclosed range: $140K to $175K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Dechert LLP AI Hiring
Dechert LLP has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $175K - $175K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
Frequently Asked Questions
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