- Страна
- США
- Зарплата
- 200 000 $ – 275 000 $
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Staff AI Engineer
Отличная вакансия с высокой зарплатой, значительной долей опционов и возможностью работать над передовыми AI-решениями в успешном финтех-стартапе. Четко прописанные задачи и сильная команда делают это предложение очень привлекательным для опытных инженеров.
Сложность вакансии
Роль уровня Staff требует не только глубоких технических знаний в области LLM и мультиагентных систем, но и умения проектировать сложные распределенные архитектуры. Высокая планка ожиданий обусловлена необходимостью внедрения AI в строго регулируемую финансовую индустрию.
Анализ зарплаты
Предложенная вилка $200k–$275k полностью соответствует рыночным стандартам для позиции Staff AI Engineer в США, особенно учитывая удаленный формат и дополнительный пакет акций.
Сопроводительное письмо
I am writing to express my strong interest in the Staff AI Engineer position at TIFIN. With over 6 years of experience in machine learning and a proven track record of deploying production-grade LLM applications, I am particularly drawn to TIFIN’s mission of building the AI operating layer for wealth management. My expertise in architecting multi-agent systems and implementing robust evaluation frameworks aligns perfectly with your current projects, such as the Advisor Copilot and the AI Agent Platform.
In my previous roles, I have focused on solving the exact challenges mentioned in your job description: ensuring reliability, grounding, and explainability in complex AI workflows. I have extensive experience with Python, modern agent frameworks, and distributed cloud infrastructure, which allows me to bridge the gap between cutting-edge AI research and enterprise-ready production systems. I am excited by the prospect of bringing my systems-thinking approach to TIFIN’s high-impact engineering team and contributing to the rapid delivery of innovative financial solutions.
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Описание вакансии
WHO WE ARE
TIFIN builds the AI operating layer for wealth. Our platform delivers agentic workflows across the industry’s core personas—investors, advisors, investment teams, and operations—so financial institutions can move faster, serve more clients, and deliver better outcomes with the same (or fewer) resources. We combine finance-native AI, specialized data, and enterprise-grade controls to deploy secure, compliant capabilities into real production environments.
WHAT SETS US APART
- Speed: We build and ship quickly—MVPs in ~3 months, production-ready products in ~6–12 months.
- Track Record: Prior exits include 55ip (acquired by J.P. Morgan) and Paralel Technologies
- Strategic Partners: Partners include J.P. Morgan, SEI, Franklin Templeton, Morningstar, Broadridge, Motive Partners and Tectonic Ventures.
- World-Class Team: Complimentary expertise across AI and financial services, with experience from Google, Microsoft, Uber, PayPal, eBay, BlackRock, LPL, Franklin Templeton, Morgan Stanley, Broadridge and more.
OUR VALUES
- Grow at the Edge. We are driven by personal growth fueled by a beginner’s mindset. We get out of our comfort zone and keep egos aside. With self-awareness and integrity we strive to be the best we can possibly be. No excuses.
- Understanding through Listening and Speaking the Truth. We communicate with authenticity, precision and integrity to create a shared understanding. We identify opportunities within constraints and propose solutions in service to the team.
- I Win for Teamwin.We believe in staying within our genius zones to succeed and taking accountability for driving results. We are all individual contributors first and always thinking about what can be better.
ROLE OVERVIEW
As we scale our AI platform, we are building production-grade multi-agent systems that power advisor copilots, investment intelligence workflows, and autonomous research capabilities. We are seeking a Staff AI Engineer to architect, build, and operationalize these systems at scale. This is a hands-on, high-impact engineering role focused on shipping reliable, enterprise-ready agentic AI systems into production.
PROJECTS
- Advisor Copilot (Multi-Agent Systems)Build a production-grade, multi-agent copilot for financial advisors that retrieves and reasons over client data, analyzes portfolio exposures and risk scenarios, generates personalized insights, enforces compliance guardrails, and drafts client-ready communications — all within a monitored, auditable architecture.
- Workflow AutomationDesign end-to-end AI workflows spanning client discovery, investment research synthesis, portfolio construction and optimization, and compliant meeting preparation — replacing fragmented tools with intelligent, autonomous systems.
- AI Agent Platform & InfrastructureArchitect a scalable multi-agent platform with orchestration engines, memory and state management, dynamic tool invocation, structured output validation, observability, fault tolerance, and automated evaluation — solving reliability, explainability, and regulatory challenges at scale.
WHAT YOU’LL DO
- Design and implement production-grade multi-agent systems using modern agent frameworks (e.g., Pydantic AI, Agent Harness, Tool-Calling, Code Execution)
- Build agent workflows that integrate context retrieval, reasoning, tool execution, validation, and compliance checks,
- Develop distributed services for agent execution with strong observability, monitoring, and failure handling
- Establish evaluation frameworks for multi-step reasoning accuracy, groundedness, hallucination mitigation, and financial correctness
- Implement memory management, context handling, and agent state persistence strategies
- Partner with product, design, and engineering teams to translate business requirements into robust agent architectures
- Optimize systems for latency, cost efficiency, and reliability in production
- Contribute to infrastructure decisions around model serving, vector databases, caching, and orchestration layers
WHAT YOU’LL BRING
- 3+ years of experience building and shipping Generative AI and LLM applications into production, 6+ years of ML experience.
- Demonstrated experience designing and deploying multi-agent systems of various architecture
- Strong experience with multimodal LLMs, knowledge graph, data synthesis, LLM fine tuning, reinforcement learning, agent harness, agent memory
- Deep proficiency in Python and modern AI frameworks
- Experience with distributed systems, cloud infrastructure (AWS/GCP/Azure), and containerized deployments
- Experience implementing monitoring, evaluation, and reliability safeguards for AI systems
- Strong systems thinking — ability to design beyond single-model solutions toward coordinated, multi-component architectures
- Resilience and adaptability - experience working at early-stage startups is a plus
COMPENSATION RANGE
$200,000 - $275,000 USD, competitive and appropriate to experience level
In addition to cash compensation, a meaningful equity stake is a significant part of the overall package. Package also includes benefits program eligibility: Comprehensive health, dental and vision coverage, retirement benefits and flexible PTO.
TIFIN is proud to be an equal opportunity workplace and values the multitude of talents and perspectives that a diverse workforce brings. All qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status.
Please see more details on our privacy practices in our Privacy Notice here.
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Навыки
- AWS
- Azure
- Python
- GCP
- Machine Learning
- LLM
- Docker
- Generative AI
- Vector Databases
- Knowledge Graph
- Reinforcement Learning
- Multi-agent systems
- Pydantic AI
Возможные вопросы на собеседовании
Проверка опыта проектирования сложных систем, что является ключевым для этой роли.
Расскажите о самой сложной мультиагентной системе, которую вы спроектировали для продакшена. С какими проблемами оркестрации вы столкнулись?
В финтехе точность критична. Вопрос проверяет умение бороться с галлюцинациями.
Какие конкретные стратегии и фреймворки оценки вы используете для обеспечения достоверности (groundedness) ответов LLM в финансовых сценариях?
Проверка навыков системного проектирования и оптимизации ресурсов.
Как бы вы спроектировали систему управления памятью и состоянием для долгоживущих агентов, чтобы минимизировать задержки и затраты на токены?
Роль Staff подразумевает работу с инфраструктурой.
Каков ваш подход к мониторингу и отладке цепочек рассуждений (reasoning chains) в реальном времени при возникновении сбоев в одном из инструментов агента?
Проверка практического опыта с современным стеком.
Сравните использование Pydantic AI с другими фреймворками (например, LangGraph или CrewAI) для построения детерминированных рабочих процессов. Почему вы выберете тот или иной вариант?
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- Страна
- США
- Зарплата
- 200 000 $ – 275 000 $