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Principal Software Engineer, AI
Высокий балл за статус компании (лидер рынка), масштаб задач (миллионы документов) и уровень влияния (Principal/Founding Engineer). Это отличная возможность для опытного инженера возглавить ИИ-направление в глобальной компании.
Сложность вакансии
Роль требует исключительного сочетания 12-летнего опыта в разработке бэкенда и глубокой экспертизы в жизненном цикле AI/ML. Высокая сложность обусловлена необходимостью проектировать системы для обработки миллионов документов ежедневно и лидерскими качествами уровня 'Principal'.
Анализ зарплаты
Указанная роль Principal уровня в Бангалоре обычно предполагает компенсацию выше среднего по рынку из-за дефицита кадров с 12+ годами опыта и глубокими знаниями ИИ. Ожидаемый доход значительно превышает стандартные предложения для Senior разработчиков.
Сопроводительное письмо
I am writing to express my strong interest in the Principal Software Engineer, AI position at AlphaSense. With over 12 years of experience in building complex backend systems and a deep focus on production-grade AI/ML lifecycles, I am excited by the opportunity to act as a foundational pillar for your Content portfolio. My background in architecting scalable distributed systems and my hands-on experience with LLMs and Computer Vision align perfectly with your mission to extract intelligent insights from millions of unstructured documents.
Throughout my career, I have specialized in bridging the gap between AI research and production-ready engineering. I have a proven track record of managing LLM latency, developing robust prompt engineering strategies, and mentoring large engineering teams. I am particularly drawn to AlphaSense's unique position in the market intelligence space and the 'founding engineer' nature of this role, which allows for significant influence over technical direction and innovation.
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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:
We are seeking a visionary and hands-on Principal AI Engineer to be the foundational pillar of our strategy in the Content portfolio. You will be the catalyst who injects deep, modern AI expertise into our product portfolio. Your mission will be to research, architect, and pioneer the solutions that will process and extract intelligent insights from millions of unstructured documents and multi-media files every single day.
This is a unique "founding engineer" opportunity to shape the future of AI within our company. You will be the primary AI/ML authority, responsible for identifying and implementing state-of-the-art techniques for document classification, entity extraction, image search, and more. If you are a seasoned engineer who thrives on solving massive-scale data challenges and wants to build a center of excellence from the ground up, this role is for you.
What You’ll Do:
- Spearhead AI Innovation: Act as the chief technical authority on AI, you will research, evaluate, and prototype cutting-edge solutions using Large Language Models (LLMs), Computer Vision, and other machine learning techniques to solve our most complex data extraction challenges.
- Architect for Scale: Design and build robust, highly scalable, and cost-effective AI services and data processing pipelines. Your architecture will be the backbone for processing millions of documents daily with high reliability and throughput.
- Tackle Real-World AI Challenges: Go beyond theory to systematically solve the practical problems of production AI. This includes managing LLM latency and variance, developing sophisticated prompt engineering strategies, and building fault-tolerant, defensive systems that perform consistently.
- Be a Force Multiplier: Act as the key technical mentor and thought leader for our large engineering team and drive some mission-critical initiatives to production
Who You Are:
Core Qualifications
- 12+ years of professional software engineering experience, with a proven track record of building complex, data-intensive, backend systems.
- Deep expertise (5+ years) in building and scaling production-grade services using modern backend frameworks such as FastAPI, Django, Sanic, Spring Boot or similar.
- Significant, hands-on experience (3+ years) in the complete lifecycle of AI/ML models: from experimentation and prototyping to deploying, monitoring, and iterating on them in a high-volume cloud environment.
- Mastery in designing large-scale distributed systems, demonstrating strong knowledge of asynchronous patterns, streaming/queuing/caching strategies, and robust observability (logging, metrics, tracing).
- Exceptional communication and leadership skills. You can articulate complex technical concepts to diverse audiences and have the ability to influence engineering direction across multiple teams without direct authority.
Nice to have
- Proficiency with modern DevOps and MLOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and automated testing frameworks.
- Hands-on experience with containerization and orchestration technologies, particularly Docker and Kubernetes.
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.
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Навыки
- FastAPI
- Django
- Sanic
- Spring Boot
- LLM
- Computer Vision
- Distributed Systems
- Docker
- Kubernetes
- MLOps
- CI/CD
- Infrastructure as Code
- Python
Возможные вопросы на собеседовании
Проверка опыта работы с высоконагруженными системами, что критично для обработки миллионов документов.
Опишите архитектуру распределенной системы, которую вы спроектировали для обработки данных в реальном времени. С какими узкими местами вы столкнулись и как их устранили?
Вакансия подчеркивает важность перехода от теории к практике в AI.
Как вы решали проблему задержки (latency) и вариативности ответов LLM в продакшн-среде?
Роль предполагает лидерство без прямого подчинения.
Приведите пример, когда вам нужно было внедрить новую технологию или архитектурный паттерн во всей компании. Как вы убеждали другие команды?
Проверка навыков работы с неструктурированными данными.
Какие стратегии вы используете для обеспечения качества данных при извлечении сущностей из миллионов неструктурированных документов?
Оценка понимания современных практик эксплуатации моделей.
Каков ваш подход к мониторингу и итеративному улучшению ML-моделей после их развертывания в облаке?
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