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Lead Data Engineer - AI/ML
Привлекательная позиция в быстрорастущем стартапе с фокусом на передовые технологии (LLM, Agentic AI). Высокий потенциал влияния на продукт, но работа требует присутствия в офисе в Калифорнии.
Сложность вакансии
Роль требует глубоких знаний в MLOps, распределенных вычислениях и архитектуре данных. Статус 'Lead' подразумевает не только техническую экспертизу, но и навыки менторства в динамичной среде стартапа.
Анализ зарплаты
Зарплата не указана, но для позиции Lead Data Engineer в Калифорнии рыночный диапазон составляет $180,000–$240,000 в год плюс опционы. Это соответствует уровню высокотехнологичных стартапов в регионе.
Сопроводительное письмо
I am writing to express my strong interest in the Lead Data Engineer position at Gruve. With over five years of experience in building robust data infrastructure and a deep focus on MLOps, I am excited about the opportunity to architect and scale enterprise-grade AI solutions for your diverse client base. My background in designing distributed data processing frameworks and implementing secure CI/CD pipelines aligns perfectly with your mission to transform enterprises into AI powerhouses.
In my previous roles, I have successfully translated complex data science requirements into production-ready engineering solutions, specifically within cloud-native environments. I am particularly drawn to Gruve's work with LLMs and agentic AI, as I have been actively developing workflows that integrate predictive models into production. I am confident that my technical leadership and passion for mentoring can help drive engineering excellence within your high-impact team.
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Описание вакансии
About Gruve
Gruve is an innovative software services startup dedicated to transforming enterprises to AI powerhouses. We specialize in cybersecurity, customer experience, cloud infrastructure, and advanced technologies such as Large Language Models (LLMs). Our mission is to assist our customers in their business strategies utilizing their data to make more intelligent decisions. As a well-funded early-stage startup, Gruve offers a dynamic environment with strong customer and partner networks.
About the Role
The Lead Data Engineer will be part of a high-impact team at Gruve building enterprise-grade AI and Machine Learning solutions across healthcare, research, and operational domains. This role focuses on architecting, building, scaling, and maintaining compute frameworks, data platforms, model implementations, and agentic AI solutions that power next-generation intelligence systems. Working closely with Data & AI leadership and cross-functional stakeholders, the Lead Data Engineer will play a critical role in operationalizing AI models securely and at scale, ensuring production readiness and long-term sustainability of the AI platform.
Key Responsibilities
- Build and maintain end-to-end data pipelines and scalable infrastructure to support ML and AI models.
- Design, implement, and optimize distributed data processing frameworks in cloud-native environments.
- Translate data science requirements into production-ready engineering solutions, including training, validating, and deploying ML models on large datasets.
- Design and manage compute frameworks, analysis tooling, and model deployment pipelines that form the core AI platform.
- Develop and maintain CI/CD pipelines and automated workflows for data and ML systems (MLOps).
- Ensure secure data handling practices aligned with enterprise compliance and security standards.
- Troubleshoot and resolve infrastructure and environment issues across production and non-production systems.
- Collaborate with engineering, platform, and security teams to ensure system reliability, scalability, and performance.
- Mentor junior engineers and enforce best practices around code quality, testing, documentation, and peer reviews.
- Make strategic recommendations on tools, programming languages, cloud platforms, and workflow orchestration technologies.
Basic Qualification
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical field (or equivalent professional experience).
- 5+ years of experience building data infrastructure for analytics or AI teams.
- Strong programming skills in SQL, Python, and/or R for processing large datasets in distributed cloud environments.
- Experience with cloud platforms (AWS, Azure, or GCP) and cloud deployment strategies.
- Experience implementing CI/CD pipelines and infrastructure automation.
- Hands-on experience with MLOps processes including model training, validation, deployment, and monitoring.
- Experience working in SaaS or enterprise-scale environments.
Preferred Qualifications
- Experience supporting regulated industry environments.
- Experience designing AI platforms that integrate predictive models into production workflows.
- Strong understanding of distributed computing frameworks (e.g., Spark, Kubernetes-based systems).
- Experience building agentic AI or LLM-driven workflows.
- Knowledge of workflow orchestration tools (e.g., Airflow, Prefect, Dagster).
- Proven ability to mentor engineers and drive engineering excellence.
- Strong collaboration skills with Data Scientists, architects, and business stakeholders.
*This position is being hired for a customer of Gruve and this is a Onsite Role.*
*Gruve is not able to provide visa sponsorship for this role. Applicants must be authorized to work in the United States without the need for current or future sponsorship.*
Why Gruve
At Gruve, we foster a culture of innovation, collaboration, and continuous learning. We are committed to building a diverse and inclusive workplace where everyone can thrive and contribute their best work. If you’re passionate about technology and eager to make an impact, we’d love to hear from you.
Gruve is an equal opportunity employer. We welcome applicants from all backgrounds and thank all who apply; however, only those selected for an interview will be contacted.
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Навыки
- SQL
- Python
- R
- AWS
- Azure
- GCP
- CI/CD
- MLOps
- Spark
- Kubernetes
- LLM
- Airflow
- Prefect
- Dagster
Возможные вопросы на собеседовании
Проверка опыта масштабирования ИИ-решений.
Опишите ваш опыт проектирования и масштабирования инфраструктуры для развертывания LLM в продакшене. С какими основными узкими местами вы сталкивались?
Оценка навыков MLOps и автоматизации.
Как вы подходите к реализации CI/CD для ML-моделей, учитывая версионирование данных и мониторинг дрейфа моделей?
Проверка владения инструментами обработки данных.
Каков ваш опыт работы с распределенными фреймворками, такими как Spark или Kubernetes, для обработки крупномасштабных наборов данных?
Оценка лидерских качеств.
Расскажите о случае, когда вам приходилось менторить младших инженеров или внедрять новые стандарты качества кода в команде. Каков был результат?
Проверка навыков работы в регулируемых отраслях (например, здравоохранение).
Как вы обеспечиваете безопасность и соответствие нормативным требованиям (например, HIPAA или GDPR) при проектировании конвейеров данных?
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