[All remote jobs](https://jobicy.com/jobs.md)[![Future PLC logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/2da60a56-221.jpg)](https://jobicy.com/company/future-plc.md)[Future PLC](https://jobicy.com/company/future-plc.md) # Applied AI Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/future-plc.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 215k–250k / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceOpen levelPublished21 Oct 2026Apply before20 Nov 2026Listing views45Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Future is hiring an Applied AI Engineer to deliver production AI capabilities for a personalized health platform. The role owns the end-to-end lifecycle of LLM agents, including prototyping, evaluation, deployment, observability, and iterative optimization. The engineer will build tool-calling systems, API integrations, structured outputs, and streaming workflows while improving quality, latency, reliability, and cost. Success requires strong Python engineering, practical LLM experience, and close collaboration with product, mobile, and backend teams. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior, hands-on production AI role requiring ownership across application engineering, LLM evaluation, cloud infrastructure, and operational reliability. The work involves ambiguous product problems and consequential trade-offs among safety, quality, latency, and cost. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$232,500US market range (per year)$190k–$270k0$297k AI insightThe employer explicitly lists a base salary range of $215,000–$250,000 per year, with a midpoint of $232,500 annually. Estimated US market compensation for a senior applied AI engineering role is approximately $190,000–$270,000 per year; equity may be additional. ## Core skills Skills and capabilities most closely associated with this opportunity. [Python](https://jobicy.com/jobs?search_keywords=Python.md)[Large Language Models](https://jobicy.com/jobs?search_keywords=Large%20Language%20Models.md)[LangChain](https://jobicy.com/jobs?search_keywords=LangChain.md)[LangGraph](https://jobicy.com/jobs?search_keywords=LangGraph.md)[Prompt Engineering](https://jobicy.com/jobs?search_keywords=Prompt%20Engineering.md)[AI Agents](https://jobicy.com/jobs?search_keywords=AI%20Agents.md)[API Integration](https://jobicy.com/jobs?search_keywords=API%20Integration.md)[Async Python](https://jobicy.com/jobs?search_keywords=Async%20Python.md)[Server-Sent Events](https://jobicy.com/jobs?search_keywords=Server-Sent%20Events.md)[Pydantic](https://jobicy.com/jobs?search_keywords=Pydantic.md)[JSON Schema](https://jobicy.com/jobs?search_keywords=JSON%20Schema.md)[LLM Evaluation](https://jobicy.com/jobs?search_keywords=LLM%20Evaluation.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md)[Terraform](https://jobicy.com/jobs?search_keywords=Terraform.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md) Sample interview questionsHow would you take a new LLM agent capability from concept to production?I would first define the user outcome, failure modes, and measurable quality criteria. I would build a narrow prototype with structured inputs and outputs, create representative evaluation cases, and validate performance before staged deployment. After release, I would monitor traces, quality metrics, latency, errors, and cost, then iterate based on production evidence. How do you make tool-calling LLM systems reliable? I use strict schemas for tool arguments and responses, validate all inputs with Pydantic or JSON Schema, and make tool behavior deterministic where possible. I also add retries with bounded backoff, idempotency where needed, clear error handling, and observability around every model and tool call. Evaluation cases should include malformed arguments, unavailable dependencies, and ambiguous user requests. What metrics would you use to evaluate an AI health-guidance feature? I would combine safety, factual grounding, personalization, task completion, user satisfaction, and escalation or refusal correctness. Operational metrics would include latency, token usage, tool success rate, fallback rate, and cost per successful interaction. I would use curated datasets, rubric-based review, and carefully calibrated LLM-as-a-judge scoring alongside human review for high-risk cases. How would you reduce the latency and cost of an agent that calls several APIs? I would identify independent tool calls and execute them concurrently with async Python, while applying timeouts and cancellation rules. I would reduce unnecessary context, use prompt caching where supported, enforce token budgets, and select models according to task complexity. Trace data would guide optimization by showing the slowest calls, redundant tool usage, and expensive prompt segments. Describe how you collaborate with product and backend teams on an AI feature. I begin by translating the product goal into clear user flows, API contracts, success metrics, and known limitations. With backend engineers, I align on schemas, authentication, failure behavior, and deployment ownership; with product, I communicate trade-offs and evaluation results in accessible terms. I keep the team informed through concise design documents, demos, and evidence from staging and production. Opportunity details ## About this role. About Us: Future is building a personalized guidance system for lifelong health. We help people understand what to do next for their body, goals, and stage of life — then support them in turning those decisions into sustained behavior change. By combining AI, human expertise, personal health data, and accountability, Future helps members improve performance today while building the resilience, capacity, and healthspan they need for decades to come. About the Role We’re looking for an Applied AI Engineer to help us build and ship AI-powered features that directly improve our product experience and business outcomes. This is a hands-on, product-focused role where you’ll take ideas from concept to production — designing intelligent systems, validating them with real users, and turning them into reliable, scalable services. You’ll work at the intersection of AI, product, and engineering — partnering closely with cross-functional teams to identify high-impact opportunities, prototype quickly, and iterate based on data. This isn’t a research-only role. You’ll own the full lifecycle: experimentation, evaluation, deployment, monitoring, and continuous improvement. The ideal candidate is excited about applying LLMs and modern ML tooling to real-world problems. You think in terms of systems, tradeoffs, and outcomes — not just models. You care about performance, quality, latency, and cost in production. Most importantly, you’re motivated by shipping impactful AI experiences that customers actually use. What You’ll Do * Build and ship AI agents that serve real users: tool-calling LLM systems with structured output, parallel API orchestration, and streaming responses. * Design evaluation harnesses and quality scoring — we use Langfuse, rubrics to measure safety, effectiveness, and personalization. * Own the full loop: prototype a new agent capability, validate it with evals, deploy it to staging and production, monitor traces, and iterate. * Improve reliability, latency, and cost through prompt caching strategies, token budgets, retry logic, and observability. * Write the tools agents use: API integrations with Pydantic validation, exercise search over local databases, structured workout submission. What You Bring * Strong Python skills: you’ve built and deployed services on large production systems. * Experience with LangChain/LangGraph or similar agent frameworks. * Hands-on experience with LLMs in production: prompt engineering, tool/function calling, structured output, evaluation. * Comfort with async Python, HTTP APIs, and streaming protocols (SSE, webhooks). * Experience with data validation and schema design (Pydantic, JSON Schema). * Ability to debug across layers: from a broken LLM tool call to a misconfigured Terraform resource. * Clear communication: you’ll work directly with product, mobile, and backend engineers. Nice to Have * Familiarity with AWS (Bedrock, ECR, CloudFront, S3, Cognito) or other cloud agent hosting. * Observability and tracing tools (Langfuse, OpenTelemetry, Datadog). * Exposure to evaluation frameworks: LLM-as-a-judge, automated scoring, dataset management. * Infrastructure-as-code (Terraform, CDK). Compensation & Benefits We believe great people should be well paid and meaningfully invested in what they’re building. Base Salary $ 215,000– $ 250,000/ year + equity. The salary range is set based on multiple considerations including business needs, market demands, talent availability, experience, and unique skills and attributes. The base pay range is subject to change and may be modified in the future. Equity Equity participation offered alongside base compensation. Health Coverage Comprehensive medical, vision, dental, and disability insurance plus tax savings accounts for all eligible employees. Retirement 401(k) plan with tax-advantaged savings options. Remote-First Employment eligible to all employees located anywhere in the continental US. No travel required. Wellness & Development Monthly health and fitness stipend contributing to overall wellbeing, access to a mental health platform, reimbursement for medical travel, and an annual learning & development stipend. Flexible Time Off Flexible PTO so you can rest, recharge, and take care of life outside of work. Future Membership Enjoy our platform for free! ### Life at Future Equity: Meaningful equity package. Health Coverage: All employees who meet eligibility requirements receive comprehensive medical, dental, vision, and disability insurance, plus tax-advantaged savings accounts. Retirement: 401(k) plan. Wellness: Monthly wellness stipend for fitness, recovery, and overall wellbeing, plus access to mental health resources and therapy support. Flexible Time Off: Flexible PTO so you can rest, recharge, and take care of life outside of work. Learning & Development: Annual budget for courses, conferences, coaching, and tools that help you grow in your craft. Future Membership: Enjoy our platform for free. ### Equal Employment Opportunities at Future We are committed to building a future where every member of our community, is healthy, cared for, and advancing forward together. Future is an equal opportunity employer. We do not discriminate based on gender, ethnicity, sexual orientation, religion, age, civil or family status, disability or race. 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