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Senior Software Engineer (AI Applications)

Оценка ИИ

AlphaSense — это престижный работодатель в сфере FinTech и AI с сильной инженерной культурой. Роль предлагает работу с передовым стеком технологий и реальное влияние на продукт, используемый компаниями из S&P 500.


Вакансия из Quick Offer Global, списка международных компаний
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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена требованиями к глубокому опыту работы с LLM (2+ года) и общим стажем разработки от 6 лет. Кандидат должен обладать редким сочетанием навыков классического NLP и современной разработки на базе трансформеров.

Анализ зарплаты

Медиана55 000 $
Рынок45 000 $ – 70 000 $
Оценка ИИ

Указанная роль Senior уровня в Дели в международной компании обычно предполагает зарплату выше среднего по рынку Индии. Ожидаемый диапазон составляет от 3.5 до 5.5 млн индийских рупий в год в зависимости от квалификации.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Software Engineer (AI Applications) position at AlphaSense. With over 6 years of experience in Python development and a specialized focus on deploying LLM-based solutions, I am confident in my ability to contribute to your Content Engineering team. My background includes extensive work with prompt engineering, structured data extraction, and building scalable data pipelines, which aligns perfectly with AlphaSense's mission to deliver trusted market intelligence.

In my previous roles, I have successfully implemented LLM observability using tools like Langfuse and managed complex microservices architectures. I am particularly drawn to AlphaSense's challenge of processing high volumes of financial documents with non-deterministic AI outputs. I am eager to bring my expertise in FastAPI, LangChain, and asynchronous data processing to help scale your document understanding capabilities and maintain the high standards your enterprise customers expect.

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Откликнитесь в alphasense уже сейчас

Присоединяйтесь к лидеру в области AI-аналитики и создавайте будущее обработки финансовых данных вместе с AlphaSense!

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:

Seeking a passionate Senior Engineer to join our Content Engineering team. This team specializes in large-scale content processing using cutting-edge technologies. In this role, you'll leverage AI applications (NLP, LLM) for document understanding, including, but not limited to, document classification, text categorization, data extraction, and semantic similarity.

Requirements:Must have

  • 6+ years of experience as a software engineer, primarily with Python
  • Ideally, you possess an advanced degree (Bachelor's / Master's)  in Computer Science, Computer Engineering, or a relevant field with a focus on Artificial Intelligence, machine learning, but equivalent industry experience may be considered
  • 2+ years of hands-on experience building and deploying applications that use Large Language Models (LLMs).
  • Experience in extracting structured JSON from text using LLMs and a deep understanding of Prompt Engineering.
  • Proficiency in classic NLP libraries for tasks like NER, text classification, and pre-processing.
  • Experience with LLM tracing, debugging and evaluation platforms such as Langfuse.
  • Excellent communication, organizational, problem-solving, debugging, and analytical skills.

Great to have

  • Experience in async processing of data at scale
  • Using frameworks like LangChain, HuggingFace, and backend web frameworks like FastAPI.
  • Expertise on core engineering skills - Cloud, microservice architecture, caching, and queuing.
  • Familiarity with concepts/tools as Docker, k8s, VCS, CI/CD
  • Previous experience in data extraction from specific domains (e.g., financial reports, news).

Responsibilities:

  • Deploy LLM based production grade solutions.
  • Fine-tune and adapt State of the art LLMs for specialized data extraction tasks. Develop innovative prompt engineering strategy and systems to maximize reliability and performance on confidential business documents.
  • Solve Engineering challenges that come along with LLM solutions, such as high latency, non-deterministic output, etc.
  • Implement and scale data processing pipelines that can process high volumes of complex documents with high accuracy and efficiency.
  • Integrate and manage LLM Observability to trace, debug, and monitor every extraction
  • Own systems end-to-end, including(but not limited to) designing, coding, monitoring, testing, CI/CD and on-call support.

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

  • Python
  • LLM
  • Kubernetes
  • CI/CD
  • Microservices
  • Prompt Engineering
  • NLP
  • Docker
  • JSON
  • FastAPI
  • LangChain
  • Huggingface
  • Caching
  • Langfuse
  • NER
  • Queuing

Возможные вопросы на собеседовании

Проверка опыта работы с основной проблемой LLM в продакшене.

Как вы справляетесь с недетерминированностью ответов LLM при извлечении структурированных данных (JSON)?

Оценка навыков оптимизации производительности систем.

Какие стратегии вы используете для снижения задержки (latency) в приложениях, использующих тяжелые языковые модели?

Проверка владения инструментами мониторинга.

Расскажите о вашем опыте работы с Langfuse или аналогичными платформами для трассировки и оценки качества работы моделей.

Оценка архитектурных навыков в контексте больших данных.

Как бы вы спроектировали конвейер обработки данных для асинхронного анализа тысяч финансовых отчетов в реальном времени?

Проверка навыков тонкой настройки моделей.

В каких случаях вы предпочтете Fine-tuning модели вместо сложного Prompt Engineering, и наоборот?

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