AI Engineer

• Design agentic and LLM-based systems that combine extraction, retrieval, reasoning, and tool use to complete multi-step work, with clear human-in-the-loop points

• Write the design docs, make the build-vs-buy and model choices, and set the success metrics for each workflow you own Build Reliable AI in Production

• Build production-grade LLM pipelines: prompt and context design, structured outputs, validation, fallbacks, and confidence scoring
• Integrate AI services cleanly with Jeeves’s backend: clear API contracts, retries, graceful degradation, and per-customer data isolation

• Manage cost and latency across models, choosing the right model for each step Measure, Monitor, and Improve

• Build evaluation sets and automated evals for every workflow you own, and use them to catch regressions when prompts, models, or data change
• Instrument AI components with logging, tracing, dashboards, and alerts so you know when quality drops before a customer does

• Keep an audit trail of AI decisions that meets the standards of a regulated financial product Raise the Bar for AI Engineering at Jeeves

• Set patterns, shared tooling, and practices that other engineers can build on
• Show the wider team how to use coding agents and AI tools well in day-to-day engineering
• Review peers’ AI system designs and share what you learn openly, Artificial Intelligence * Big Data * Healthtech * Information Technology * Machine Learning * Software * Analytics Designs and deploys enterprise AI/ML solutions, scalable models, data pipelines, and MLOps architectures in cloud production environments. Responsibilities include model monitoring and optimization, responsible AI implementation, workflow automation, emerging technology evaluation, cross-functional architecture collaboration, code reviews, and mentoring. The role focuses on healthcare challenges and supports production-grade machine learning systems, including real-time inference and continuous integration and deployment. Top Skills: AirflowAWSAzureCi/CdDockerFhirGCPGenerative AiHl7KafkaKubeflowKubernetesLarge Language ModelsMlflowMlopsPythonPyTorchRetrieval-Augmented GenerationScikit-LearnSparkSQLTensorFlow CrowdStrike

Sr. AI Agent Developer (Remote)

4 Days Ago Remote or Hybrid USA 160K-250K Annually Senior level 160K-250K Annually Senior level Cloud * Computer Vision * Information Technology * Sales * Security * Cybersecurity Design, build, deploy, and scale production AI agents and platforms for legal workflows. Develop and evaluate agent solutions, optimize performance and cost, redesign business processes, and promote responsible AI adoption. Collaborate with Legal, Enterprise AI, Data, Security, Privacy, and Engineering teams to deliver secure, reliable, scalable solutions. Contribute frameworks, training, governance guidance, and innovative applications using modern LLM and AI techniques. Top Skills: Advanced RetrievalAgent FrameworksAi AgentsAi GatewaysAi/MlAWSFine-TuningGCPKnowledge GraphsLarge Language ModelsModel Context ProtocolModel CustomizationMulti-Agent SystemsMultimodal Document ProcessingPythonSecure Tool IntegrationsSynthetic Data Mastercard

• 7+ years of professional software engineering experience, including at least 2 years building and operating LLM or AI-powered systems in production
• A track record of owning a large system or workflow end to end, from scoping and design through launch and iteration, with limited direction
• Hands-on experience shipping LLM-powered applications with APIs such as Anthropic, OpenAI, or similar, including structured outputs, error handling, and evaluation
• Experience designing agentic or multi-step AI workflows (tool use, orchestration, human review steps)
• Strong proficiency in Python, plus solid backend fundamentals: REST APIs, PostgreSQL or similar relational databases, async patterns, and a major cloud provider (AWS, GCP, or Azure)
• Experience with observability for AI systems: logging, tracing, dashboards, and quality monitoring
• Professional fluency in English, written and spoken

Own AI workflows end to end, from system design and model selection through production deployment, evaluation, monitoring, and continuous improvement. Build reliable agentic and LLM-powered applications with structured outputs, tool use, human review, fallbacks, observability, and audit trails. Integrate AI services with backend systems while managing cost, latency, security, and customer data isolation. Establish engineering standards, mentor peers, and help teams use AI coding tools effectively. The summary above was generated by AI, 5 Days Ago Remote or Hybrid 212K-407K Annually Senior level 212K-407K Annually Senior level Blockchain * Fintech * Payments * Consulting * Cryptocurrency * Cybersecurity * Quantum Computing Architect and engineer secure, scalable enterprise AI infrastructure for LLM training and inference, machine learning, agentic AI, and production platforms. Lead HPC, GPU, Kubernetes, cloud, networking, storage, reliability, observability, security, and automation strategies. Resolve complex performance and scalability challenges, establish engineering standards, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineers in a regulated environment. Top Skills: Accelerated ComputingAgentic AiCloud-Native PlatformsDistributed SystemsGpuHigh-Performance NetworkingHpcHybrid CloudInfrastructure AutomationKubernetesLlmsMachine LearningObservabilityPrivate CloudRack-Scale SystemsSreStorage

What you need to know about the Colorado Tech Scene

With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.

Key Facts About Colorado Tech

• Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
• Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
• Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
• Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
• Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
• Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute

Jeeves is the stablecoin-native banking platform for global enterprises. Thousands of companies across 35 countries run their corporate cards, payments, treasury and spend on one platform, with more than $5B in annualized volume, payouts to 190 countries, and one of the fastest-growing stablecoin payment networks in the world. BMW, H&M, Lululemon, Burger King, Kavak and XP are among them, and more than 80% of customers use multiple Jeeves products.

We chose hard problems on purpose.

We hire for one thing: relentlessness. A strong motor that keeps going when the problem has no playbook. You’ll know in your first week if Jeeves is right for you. For the right people, it’s the steepest learning curve of your career.

Jeeves has raised more than $400 million in equity funding from Andreessen Horowitz, Y Combinator, CRV, Coinbase Ventures, GIC, AllianceBernstein, CoinFund and others.

If you want to rebuild how money moves across borders, come build it with us.

As an AI Engineer at Jeeves, you will own whole workflows from end to end, not individual features. You’ll decide how to build it, ship it, measure it, and keep improving it. You’ll work closely with Product, Backend Engineering, and our customers, but you won’t wait for someone to hand you a spec.

This is a senior, hands-on role for people who are AI-native in how they work. You use coding agents and AI tools every day to move faster, and you know where they fall short. It isn’t a research role, and it isn’t a role for someone who wants a narrow, well-defined ticket queue.