{"id":976876,"url":"https://alion.io/job/charles-river-associates-full-stack-agentic-developer","title":"Full Stack Agentic Developer","company":{"id":723171,"name":"Charles River Associates","domain":"crai.com","url":"https://alion.io/company/charles-river-associates","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"C","score":68,"open_postings":65,"ghost_share":0.446,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":161,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Boston, United States","New York, United States","Chicago, United States","Washington, United States","Oakland, United States","Dallas, United States","Houston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":230000,"currency":"USD","period":"year","gross":null,"usd_annual":230000},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Azure","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Caching","optional":false},{"name":"React.js","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false},{"name":"Vite","optional":false},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"GitHub Actions","optional":true},{"name":"JavaScript","optional":true},{"name":"Linux","optional":true},{"name":"LLM Evaluation","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"Python","optional":true},{"name":"Tailwind CSS","optional":true},{"name":"Ubuntu","optional":true},{"name":"WebSockets","optional":true}],"status":"live","first_seen_at":"2026-06-16T20:08:37Z","employer_posted_date":"2026-09-01","last_verified_at":"2026-10-16T17:49:13Z","board_verified":true,"closed_at":null,"days_open":121,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":121},"description":"About Charles River Associates\nCharles 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.\nPosition Overview\nCharles 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.\nThe 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.\nThe 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.\nCore Mission\nThe 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.\nKey Responsibilities\nFull Stack Product Development\nBuild 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.\nCreate reusable component patterns for complex, stateful workflows while keeping the application maintainable, accessible, and easy for the team to extend.\nBuild and maintain Express/TypeScript API endpoints that support the web application, including session orchestration, file management, workspace operations, usage tracking, and new product capabilities.\nIntegrate frontend workflows with backend services for authentication, LLM routing, usage tracking, agent orchestration, cloud storage, and PostgreSQL-backed application data.\nTranslate complex backend and agent states into intuitive interface patterns, including empty states, progress states, error states, review states, and resumable workflows.\nReal-Time Agent Experience\nImplement and improve the real-time streaming interface between the backend, agent runtime, and UI, primarily through server-sent events and related event-driven patterns.\nRender 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.\nManage stream connection lifecycle, retries, cancellation, cooperative stop, stop/resume behavior, error recovery, and clear feedback when long-running agent workflows are in progress.\nHelp define event contracts so the UI can present agent behavior clearly without exposing unnecessary implementation complexity to end users.\nDesign 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.\nAgentic Runtime and Tooling\nOwn 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.\nDevelop proprietary tools that expand agent capabilities across file operations, analysis, transformation, visualization, document creation, data processing, validation, and workflow automation.\nExtend the containerized execution environment to support new languages, libraries, utilities, file types, analytical methods, and integrations needed by expert users.\nDesign clear tool schemas, permission boundaries, workspace access patterns, input validation, and error messages that help agents use tools effectively and safely.\nCreate reusable patterns for adding new tools so agent capabilities can expand without making the runtime brittle, opaque, or hard to debug.\nLLM Provider, Prompt, and Context Systems\nSupport 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.\nBuild and maintain translation layers that normalize provider differences while preserving access to the strongest capabilities of each model.\nDesign 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.\nImplement token estimation, usage tracking, context compression, conversation summarization, prompt caching, and model-selection patterns for long-running analytical sessions.\nEvaluate 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.\nSession, File, and Workspace Lifecycle\nOwn the user-facing lifecycle of an AI work session, including creation, configuration, file upload, streaming execution, interruption, resumption, result review, workspace cleanup, and teardown.\nImplement browser-to-cloud file flows including multi-file upload, progress tracking, validation, workspace browsing, previewing, downloading, and handling of large or mixed file types.\nSupport interfaces and APIs that help users understand the state of remote workspaces, generated outputs, intermediate artifacts, source files, and final deliverables.\nImprove the connection between workspace state and agent state so users can review work product clearly and engineers can debug session behavior reliably.\nReliability, Security, and Observability\nImplement reliability patterns such as retry logic, rate-limit handling, tool error recovery, cooperative stop, graceful cancellation, resumability, and failure reporting.\nBuild 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.\nAdd structured logging, tracing, transcript capture, metrics, tests, and debugging tools so agent behavior can be understood at both the engineering and product level.\nPartner 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.\nContribute to delivery discipline by writing clear technical notes, estimating work thoughtfully, supporting sprint planning, and continuously improving development practices.\nDesired Qualifications\nBachelor's degree in Software Engineering, Engineering, or other relevant discipline with programming/technology experience, advanced degree desirable;\n6+ years of professional software engineering experience, with strong hands-on ownership across frontend, backend, and production product systems;\nStrong TypeScript skills across the stack, including modern React development and Node.js/Express API development;\nExperience building component-driven React applications with complex state, multiple interconnected views, real-time updates, and user-facing workflows that require careful error handling;\nExperience building or consuming real-time interfaces using server-sent events, WebSockets, streaming APIs, or similar event-driven patterns;\nExperience building LLM-powered agentic systems that use tools, execute multi-turn workflows, manage state, and recover from errors; not just experience building static chatbots;\nExperience with LLM tool calling or function calling, including tool schema design, streaming tool input/output, multi-turn execution, and provider-specific implementation details;\nStrong prompt engineering ability for structured, multi-step workflows, including prompts that encode policies, methodology, roles, and tool usage expectations;\nComfort working in Python for agent tools, data processing, automation, evaluation, and integration with analytical libraries;\nGood understanding of browser authentication flows, JWT lifecycle, token refresh, CORS, secure cookies, role-based access, and frontend/backend security boundaries;\nFamiliarity with PostgreSQL and API-driven application design, including practical awareness of schema design, queries, migrations, and data access patterns;\nExperience with Docker or Linux-based execution environments and practical understanding of isolation, filesystem access, dependency management, and runtime troubleshooting;\nStrong product judgment, debugging instincts, documentation discipline, and ability to reason about AI behavior, software behavior, and user impact at the same time.\nStrongly Preferred Experience\nExperience designing custom agent frameworks, agent runtimes, orchestration loops, tool-extension systems, or evaluation harnesses rather than relying entirely on off-the-shelf frameworks.\nExperience with OpenAI, Anthropic, and other model provider APIs, including streaming, tool use, structured outputs, usage tracking, rate limits, and provider-specific failures.\nExperience with file-heavy web applications, including upload progress, large file handling, previewing, workspace navigation, generated-output review, and download flows.\nExperience rendering markdown, structured outputs, tool activity, logs, transcripts, plans, or other rich incremental content in React.\nExperience with sandboxed or ephemeral compute patterns, dynamically provisioned containers, secure credential injection, and session-scoped runtime lifecycles.\nExperience with Azure services such as Static Web Apps, Container Apps, Container Instances, Blob Storage, Azure Database for PostgreSQL, Application Insights, or related cloud services.\nExperience with Docker, GitHub Actions, CI/CD practices, structured logging, cloud observability, and collaboration in a distributed engineering environment.\nExperience with headless browser automation, document generation, data analysis, visualization, file conversion, R, LaTeX, or workflow automation tools.\nExperience working in consulting, professional services, legal, economic, healthcare, life sciences, energy, financial services, or other confidential/high-stakes environments.\nFamiliarity with responsible AI practices, model evaluation, transcript review, quality controls, AI governance, and enterprise AI adoption.\nCore Environment\nFrontend: TypeScript, React, Vite, Tailwind CSS\nBackend: Node.js, Express, TypeScript\nAgent runtime: Custom multi-turn agent loop, proprietary tools, model-provider adapters, and streaming event protocol\nLanguages: TypeScript, Node.js, Python; wi...","description_format":"text","description_chars":15014,"description_truncated":true,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Parental leave","Retirement plans","Vision insurance","Wellness"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Legal Services","Management Consulting"],"lifecycle":[{"event":"open","at":"2026-09-16T19:48:17Z"}],"visa":[],"liveness":{"score":49,"band":"ok","label":"Likely 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