Interested in this AI/ML Engineer role at Charles River Associates?
Apply Now →Skills & Technologies
About This Role
About Charles River Associates
Charles River Associates is a leading global consulting firm that provides economic, financial, and business management expertise to major law firms, corporations and governments around the world. CRA advises clients on economic and financial matters pertaining to litigation and regulatory proceedings, and guides corporations through critical business strategy and performance\-related issues. Since 1965, clients have engaged CRA for its combination of industry experience and rigorous, fact\-based analysis that provide clients with clear, implementable solutions to complex business concerns.
Position Overview
Charles River Associates is seeking a Full Stack Agentic Developer to help build and evolve a web\-based AI platform that enables experts to translate domain expertise into scalable, validated AI\-driven workflows. This is a senior full stack engineering role for someone who began with strong product and platform skills and has advanced into LLM\-powered agentic systems.
The product combines a React/Vite web client, a Node.js/TypeScript backend proxy, Azure\-hosted services, and isolated remote execution environments where AI agents run tools against sandboxed project workspaces. This role bridges the application layer and the agent layer: user experience, APIs, session and file workflows, real\-time streaming, custom agent runtime behavior, proprietary tools, model\-provider integrations, prompt and context systems, reliability, and observability.
The ideal candidate is not a narrow frontend engineer, not a pure backend engineer, and not a prompt\-only AI specialist. CRA needs a hands\-on full stack developer who can design excellent product experiences, write production\-grade TypeScript and React, extend Node.js/Express APIs, and also understand how agentic systems plan, use tools, recover from errors, stream activity, and produce work that users can inspect and trust.
Core Mission
The Full Stack Agentic Developer will own the path from user intent to agent action to reviewed output. This person will build the product workflows and underlying agentic capabilities that let consultants and domain experts create sessions, upload and organize materials, launch AI\-assisted work, monitor agent activity in real time, review files and intermediate outputs, and rely on the platform for high\-quality analytical work in confidential, high\-stakes environments.
Key Responsibilities
#### Full Stack Product Development
- Build and evolve the React web application across core product surfaces: authentication, session setup, workspace navigation, file review, streaming agent activity, results review, and user\-facing administration.
- Create reusable component patterns for complex, stateful workflows while keeping the application maintainable, accessible, and easy for the team to extend.
- Build and maintain Express/TypeScript API endpoints that support the web application, including session orchestration, file management, workspace operations, usage tracking, and new product capabilities.
- Integrate frontend workflows with backend services for authentication, LLM routing, usage tracking, agent orchestration, cloud storage, and PostgreSQL\-backed application data.
- Translate complex backend and agent states into intuitive interface patterns, including empty states, progress states, error states, review states, and resumable workflows.
#### Real\-Time Agent Experience
- Implement and improve the real\-time streaming interface between the backend, agent runtime, and UI, primarily through server\-sent events and related event\-driven patterns.
- Render incremental agent output such as token\-by\-token text, tool execution cards, plans, task lists, progress indicators, cost and usage indicators, file changes, warnings, and final workflow states.
- Manage stream connection lifecycle, retries, cancellation, cooperative stop, stop/resume behavior, error recovery, and clear feedback when long\-running agent workflows are in progress.
- Help define event contracts so the UI can present agent behavior clearly without exposing unnecessary implementation complexity to end users.
- Design UX and API patterns that help users understand what the agent is doing, what files it has changed, what outputs are ready for review, and what still requires human judgment.
#### Agentic Runtime and Tooling
- Own and improve parts of the custom multi\-turn agent loop where the agent sends messages to model providers, parses streaming responses, executes tools, observes results, and iterates within an isolated cloud container.
- Develop proprietary tools that expand agent capabilities across file operations, analysis, transformation, visualization, document creation, data processing, validation, and workflow automation.
- Extend the containerized execution environment to support new languages, libraries, utilities, file types, analytical methods, and integrations needed by expert users.
- Design clear tool schemas, permission boundaries, workspace access patterns, input validation, and error messages that help agents use tools effectively and safely.
- Create reusable patterns for adding new tools so agent capabilities can expand without making the runtime brittle, opaque, or hard to debug.
#### LLM Provider, Prompt, and Context Systems
- Support multi\-model integration across OpenAI, Anthropic, and other frontier or local models, including provider\-specific message formats, tool\-calling formats, streaming behavior, structured outputs, and error patterns.
- Build and maintain translation layers that normalize provider differences while preserving access to the strongest capabilities of each model.
- Design and maintain prompt and context systems that shape agent behavior, including analytical identity, methodology compliance, interaction modes, tool usage policies, quality standards, and escalation patterns.
- Implement token estimation, usage tracking, context compression, conversation summarization, prompt caching, and model\-selection patterns for long\-running analytical sessions.
- Evaluate how agent behavior changes across providers, model families, prompts, tools, and workflows, then adapt the product and runtime to improve quality, cost, speed, reliability, privacy, and user trust.
#### Session, File, and Workspace Lifecycle
- Own the user\-facing lifecycle of an AI work session, including creation, configuration, file upload, streaming execution, interruption, resumption, result review, workspace cleanup, and teardown.
- Implement browser\-to\-cloud file flows including multi\-file upload, progress tracking, validation, workspace browsing, previewing, downloading, and handling of large or mixed file types.
- Support interfaces and APIs that help users understand the state of remote workspaces, generated outputs, intermediate artifacts, source files, and final deliverables.
- Improve the connection between workspace state and agent state so users can review work product clearly and engineers can debug session behavior reliably.
#### Reliability, Security, and Observability
- Implement reliability patterns such as retry logic, rate\-limit handling, tool error recovery, cooperative stop, graceful cancellation, resumability, and failure reporting.
- Build with enterprise readiness in mind, including secure browser authentication flows, JWT lifecycle, role\-based access, CORS, CSRF considerations, auditability, privacy\-sensitive UI patterns, and careful handling of confidential work product.
- Add structured logging, tracing, transcript capture, metrics, tests, and debugging tools so agent behavior can be understood at both the engineering and product level.
- Partner with product, domain experts, backend, infrastructure, and security stakeholders to ensure end\-to\-end features are reliable across the browser, API layer, cloud services, and agent execution environment.
- Contribute to delivery discipline by writing clear technical notes, estimating work thoughtfully, supporting sprint planning, and continuously improving development practices.
Desired Qualifications
- Bachelor's degree in Software Engineering, Engineering, or other relevant discipline with programming/technology experience, advanced degree desirable;
- 6\+ years of professional software engineering experience, with strong hands\-on ownership across frontend, backend, and production product systems;
- Strong TypeScript skills across the stack, including modern React development and Node.js/Express API development;
- Experience building component\-driven React applications with complex state, multiple interconnected views, real\-time updates, and user\-facing workflows that require careful error handling;
- Experience building or consuming real\-time interfaces using server\-sent events, WebSockets, streaming APIs, or similar event\-driven patterns;
- Experience building LLM\-powered agentic systems that use tools, execute multi\-turn workflows, manage state, and recover from errors; not just experience building static chatbots;
- Experience with LLM tool calling or function calling, including tool schema design, streaming tool input/output, multi\-turn execution, and provider\-specific implementation details;
- Strong prompt engineering ability for structured, multi\-step workflows, including prompts that encode policies, methodology, roles, and tool usage expectations;
- Comfort working in Python for agent tools, data processing, automation, evaluation, and integration with analytical libraries;
- Good understanding of browser authentication flows, JWT lifecycle, token refresh, CORS, secure cookies, role\-based access, and frontend/backend security boundaries;
- Familiarity with PostgreSQL and API\-driven application design, including practical awareness of schema design, queries, migrations, and data access patterns;
- Experience with Docker or Linux\-based execution environments and practical understanding of isolation, filesystem access, dependency management, and runtime troubleshooting;
- Strong product judgment, debugging instincts, documentation discipline, and ability to reason about AI behavior, software behavior, and user impact at the same time.
Strongly Preferred Experience
- Experience designing custom agent frameworks, agent runtimes, orchestration loops, tool\-extension systems, or evaluation harnesses rather than relying entirely on off\-the\-shelf frameworks.
- Experience with OpenAI, Anthropic, and other model provider APIs, including streaming, tool use, structured outputs, usage tracking, rate limits, and provider\-specific failures.
- Experience with file\-heavy web applications, including upload progress, large file handling, previewing, workspace navigation, generated\-output review, and download flows.
- Experience rendering markdown, structured outputs, tool activity, logs, transcripts, plans, or other rich incremental content in React.
- Experience with sandboxed or ephemeral compute patterns, dynamically provisioned containers, secure credential injection, and session\-scoped runtime lifecycles.
- Experience with Azure services such as Static Web Apps, Container Apps, Container Instances, Blob Storage, Azure Database for PostgreSQL, Application Insights, or related cloud services.
- Experience with Docker, GitHub Actions, CI/CD practices, structured logging, cloud observability, and collaboration in a distributed engineering environment.
- Experience with headless browser automation, document generation, data analysis, visualization, file conversion, R, LaTeX, or workflow automation tools.
- Experience working in consulting, professional services, legal, economic, healthcare, life sciences, energy, financial services, or other confidential/high\-stakes environments.
- Familiarity with responsible AI practices, model evaluation, transcript review, quality controls, AI governance, and enterprise AI adoption.
Core Environment
- Frontend: TypeScript, React, Vite, Tailwind CSS
- Backend: Node.js, Express, TypeScript
- Agent runtime: Custom multi\-turn agent loop, proprietary tools, model\-provider adapters, and streaming event protocol
- Languages: TypeScript, Node.js, Python; with R, LaTeX, and shell utilities available in execution environments
- Model providers: OpenAI, Anthropic, and other frontier or local models as needed
- Data: PostgreSQL and API\-driven application state
- Streaming: Server\-sent events and event\-driven UI updates
- Runtime: Containerized Ubuntu\-based agent environments with sandboxed project workspaces
- Cloud: Microsoft Azure\-hosted web, backend, storage, database, container, and observability infrastructure
- Product context: Expert\-driven AI workflows where transparency, reliability, confidentiality, and quality control are critical
Career Growth and Benefits
- CRA's robust skills development programs, including a commitment to offering 100 hours of training annually through formal and informal programs, encourage you to thrive as an individual and team member. Training encompasses technical training, presentation skills, internal seminars, and career mentoring and performance coaching from an assigned senior colleague. Additional leadership and collaboration opportunities exist through internal firm development activities.
- We offer a comprehensive total rewards program including a superior benefits package, wellness programming to support physical, mental, emotional and financial well\-being, and in\-house immigration support for foreign nationals and international business travelers.
Work Location Flexibility
CRA creates a work environment that enables our colleagues to benefit from being together in the office to best deliver on our promise of career growth, mentorship and inclusivity. At the same time, we recognize that individuals realize a range of benefits when working from home periodically. We currently expect that individuals spend at least 3 to 4 days a week working in the office (which may include traveling to another CRA office or to client meetings), with specific days determined in coordination with your practice or team.
Our Commitment to Equal Employment Opportunity
Charles River Associates is an equal opportunity employer (EOE). All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, status as a protected veteran, or any other protected characteristic under applicable law.
*Salary and other compensation*
*A good\-faith estimate of the annual base salary range for this position is $160,000 \- $230,000\. Stating pay within this range may vary based on factors such as education level, experience, skills, geographic location, market conditions, and other qualifications of the successful candidate. This position may be eligible for additional bonus incentive compensation.* *CRA offers a comprehensive benefits package, subject to eligibility requirements, which may include: medical, dental, and vision insurance; 401(k) retirement plan with employer match; life and disability insurance; paid time off (vacation, sick leave, holidays); paid parental leave; wellness programs and employee assistance resources; and commuter benefits.*
Salary Context
This $160K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Charles River Associates, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($195K) sits 5% above the category median. Disclosed range: $160K to $230K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Charles River Associates AI Hiring
Charles River Associates has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $230K - $230K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.