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Staff AI Platform Engineer
Отличная позиция в стабильной компании-единороге с интересными задачами на стыке Platform Engineering и AI. Высокий уровень ответственности и влияния на продукт.
Сложность вакансии
Высокая сложность обусловлена требованием 8+ лет опыта, необходимостью глубокой экспертизы в распределенных системах и Kubernetes, а также ролью лидера, отвечающего за архитектуру AI-платформы.
Анализ зарплаты
Зарплата для позиции Staff уровня в Бангалоре в международных продуктовых компаниях обычно находится в верхнем сегменте рынка. Предлагаемый диапазон соответствует уровню Tier-1 компаний в данном регионе.
Сопроводительное письмо
I am writing to express my strong interest in the Staff AI Platform Engineer position at AlphaSense. With over 8 years of experience in architecting distributed systems and a proven track record of improving system reliability to 99.9%+ uptime, I am confident in my ability to elevate your AI infrastructure. My background in Kubernetes, microservices, and infrastructure as code aligns perfectly with your mission to build robust, production-grade AI systems.
In my previous roles, I have successfully led technical initiatives that bridged the gap between ML research and production-ready software. I am particularly excited about AlphaSense's work with agentic systems and multi-model AI. I look forward to bringing my expertise in observability and CI/CD to ensure your cutting-edge AI applications run reliably for enterprise customers while mentoring the next generation of engineers on your team.
Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в alphasense уже сейчас
Присоединяйтесь к AlphaSense, чтобы возглавить разработку AI-платформы нового поколения и масштабировать инновации для лидеров рынка!
Описание вакансии
About AlphaSense:
The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content.
The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S&P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us!
About the Role
AlphaSense is seeking an experienced engineering leader to transform how we build and operate AI-powered systems at scale. You'll join a team of brilliant engineers who've built cutting-edge AI applications, and your mission will be to bring world-class engineering practices that ensure these innovations run reliably for our enterprise customers.
This is a unique opportunity for a seasoned engineer who thrives on building robust platforms, mentoring talented teams, and establishing engineering excellence. You'll have significant autonomy to shape our technical architecture while working with frontier AI models and agentic systems that power market intelligence for the world's leading companies.
What You'll Do
- Architect for Scale: Design and implement distributed systems that power AI agents processing thousands of requests per hour, ensuring reliability, performance, and cost-efficiency
- Build Engineering Excellence: Establish comprehensive testing strategies, observability systems, and CI/CD pipelines that catch issues before customers do
- Lead Through Expertise: Mentor a team of smart, motivated engineers, sharing your experience in building production systems that don't break at 3 AM
- Drive Platform Evolution: Own the technical roadmap for our AI platform, making architectural decisions that will shape our systems for years to come
- Bridge AI and Engineering: Collaborate with ML engineers and researchers to productionize cutting-edge AI capabilities while maintaining system stability
What We're Looking For
Required:
- 8+ years building and operating distributed systems in production
- Track record of improving system reliability (taking services from frequent outages to 99.9%+ uptime)
- Deep expertise in modern engineering practices: microservices, containerization (Kubernetes), infrastructure as code
- Experience leading technical initiatives and mentoring engineering teams
- Strong coding skills with ability to work across the stack
- Excellence in debugging production issues and implementing comprehensive observability
- History of making pragmatic trade-offs between perfect and shipped
Preferred:
- Experience with LLM applications, agent frameworks, or AI/ML infrastructure
- Familiarity with prompt engineering, RAG patterns, vector databases
- Background at high-growth companies or modern engineering cultures
- Experience with multi-model AI systems (OpenAI, Anthropic, Google, etc.)
Impact You'll Have
Within your first year, you'll transform how we build and operate software. You'll reduce incidents, establish testing and observability standards that become part of our DNA, and build platforms that enable other engineers to ship faster and safer. Most importantly, you'll elevate an entire team of engineers, sharing the expertise that only comes from years of building systems at scale.
AlphaSense is an equal-opportunity employer. We are committed to a work environment that supports, inspires, and respects all individuals. All employees share in the responsibility for fulfilling AlphaSense’s commitment to equal employment opportunity. AlphaSense does not discriminate against any employee or applicant on the basis of race, color, sex (including pregnancy), national origin, age, religion, marital status, sexual orientation, gender identity, gender expression, military or veteran status, disability, or any other non-merit factor. This policy applies to every aspect of employment at AlphaSense, including recruitment, hiring, training, advancement, and termination.
In addition, it is the policy of AlphaSense to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations, and ordinances where a particular employee works.
Recruiting Scams and Fraud
We at AlphaSense have been made aware of fraudulent job postings and individuals impersonating AlphaSense recruiters. These scams may involve fake job offers, requests for sensitive personal information, or demands for payment. Please note:
- AlphaSense never asks candidates to pay for job applications, equipment, or training.
- All official communications will come from an @alpha-sense.com email address.
- If you’re unsure about a job posting or recruiter, verify it on our Careers page.
If you believe you’ve been targeted by a scam or have any doubts regarding the authenticity of any job listing purportedly from or on behalf of AlphaSense please contact us. Your security and trust matter to us.
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Kubernetes
- Microservices
- Infrastructure as Code
- CI/CD
- Distributed Systems
- LLM
- RAG
- Vector Databases
- Python
- Observability
Возможные вопросы на собеседовании
Проверка опыта обеспечения надежности систем, что является ключевым требованием вакансии.
Расскажите о случае, когда вы подняли аптайм критического сервиса с низких показателей до 99.9%. Какие конкретно шаги вы предприняли?
Оценка навыков проектирования систем для AI-агентов.
Как бы вы спроектировали архитектуру для обработки тысяч запросов к LLM-агентам в час, учитывая ограничения по rate limits и задержкам?
Проверка лидерских качеств и умения развивать команду.
Опишите ваш подход к менторству опытных инженеров. Как вы внедряете культуру инженерного совершенства в уже сложившейся команде?
Оценка практического опыта работы с современным AI-стеком.
Какие основные проблемы вы видите при выводе RAG-систем в продакшн и как вы предлагаете их решать на уровне платформы?
Проверка умения находить баланс между скоростью и качеством.
Приведите пример, когда вам пришлось пойти на прагматичный компромисс между 'идеальной архитектурой' и необходимостью быстрого релиза. Чем вы руководствовались?
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