AI Tech Lead

Stealth-mode AI-powered Cloud-Native Health-Tech company is looking for a great, long-term, True Senior AI Tech Lead.

It’s not vaporware, their platform supports US Physician Networks (IPAs) by enabling Smarter, Risk-Adjusted, and more Predictive Care that improves real patient outcomes.

You will join an international team of first-class professionals who are passionate about creating products that improve the quality of medical services.

Target Stack:

Core Infrastructure (GCP Services)

• Google Cloud Platform (GCP)

• Google Kubernetes Engine (GKE)

• BigQuery

• Cloud Composer (Airflow, DBT)

• Dataproc Serverless (PySpark, SparkML, PyTorch)

• Bigtable

• Spanner

• Pub/Sub

Development & Deployment

• Python (SciPy, Pandas, pytest, FastAPI)

• Git (GitHub)

• Google Cloud Build

• Terraform

• SonarSource

AI & Advanced Analytics

• Vertex AI

• LLMs and GenAI

• Agent Development Kit (ADK)

Collaboration & Productivity

• GSuite

• LucidChart

• Slack

• Jira

NOTE: Similar Cloud-Native Experience is always an option.

Required Experience:

AI & Leadership

• Seven or more (7+) years of Experience in Applied ML and AI engineering, including three or more (3+) years in a Technical Leadership role.

• Proven track record of Leading ML initiatives; from R&D and Prototyping to Shipment, Deployment, and Productivization.

• Strong Communication and Mentoring skills across Technical and Business Domains.

• Upper-Intermediate English or higher; ability to present work and lead discussions with US-based teammates, customers, and stakeholders.

Cloud-Native & GCP

• Cloud-Native Experience – at least three or more (3+) years – is required. Preferably, GCP. The highly-proficient [in] GCP candidates will always be prioritized over Azure, AWS, and other Cloud Platforms.

• We don’t – at all – consider Legacy-only Big Data Experts. Meaning, it’s not enough to know outdated technologies, such as Teradata, Hadoop, Spark over Hadoop, etc.

• This role requires working in a Unix-like Development Environment (e.g., macOS, Linux).

Engineering & Fundamentals

• Deep knowledge of Fundamentals, such as Mathematics, Statistics, Machine Learning, Algorithms and Data Structures, etc, is required.

• Cloud-Native (GCP) is always prioritized higher than self-hosted, on-premise, or homemade over Virtual Machines solutions. There are exceptions, such as we’re keenly trying to avoid Fully Serverless (Cloud Functions or Cloud Run over Pub/Sub or GCS) solutions.

• Familiarity with Managed AI (e.g., Vertex AI) is a strong advantage.

• Experience with Generative AI and LLMs, such as OpenAI, Gemini, Claude, Seedream, GPT Image, Veo3, Sora, is required.

• Experience with Industry Standards, i.e., PyTorch, Pandas, XGBoost, LightGBM, CatBoost, Temporal Models, Classification, Transformer Architecture, SOTA Models and the Hugging Face Ecosystem.

• Strong Productivization skills are required - the ability to take ML and LLM solutions beyond prototypes and into real, production environments.

• Ability to adhere to an Iterative Development and Shipment of MVPs is required at the same time. It’s not possible to work in a Waterfall-like manner.

• Proficiency with MLOps and DevOps Solutions, such as Google Kubernetes Engine, Docker, Google Cloud Build. Other examples are MLFlow and ClearML, Feature Storing, and Grafana.

• Strong knowledge of Python and SQL. The focus is on writing Pythonic Solutions and a Style Guide-compliant SQL over BigQuery. SonarSource software is a ready-to-use helper. It’s definitely possible to write some bits in Go or Scala, where those PLs are really applicable, though the default PL is Python.

• Strong knowledge of Data Architecture and DBs Internals, including DDL, Clustering, Partitioning, Query Optimization, etc.

• Lakehouse-first Data Engineering (BigQuery, Cloud Composer, DBT) and Decoupled Distributed Data Processing are always prioritized higher than running Imperative Solutions over GKE or Coupled Massively Parallel Processing Compute.

• Imperative Code Solutions – including Classical Algorithms and Data Structures –, implemented over Dataflow or Spark are expected to come up only when the Lakehouse-first Approach isn’t applicable or is too costly.

• There are many other Experience Advantages candidates may have, e.g., Kafka, Apache Beam (Dataflow) Streaming, Spark Streaming, Python’s asyncio, Data and Model Versioning, Terraforms, etc.

Responsibilities:

• Leading and Mentoring a multidisciplinary AI team of Data Scientists, ML Engineers, Data Analysts, etc. A Tech Product Manager will be assisting with the day-to-day work.

• Leading R&D initiatives and Productivization.

• Assisting with Architectural and Engineering decisions. Assisting with choices of Tech Standards, Code Quality, and MLOps Best Practices.

• Ensuring Scalability, Reliability, and Alignment of the AI Infrastructure with GCP. Meaning, application of sensible Cloud-Native technologies, such as BigQuery, Vertex AI, Cloud Composer, etc; instead of writing self-hosted homemade solutions running on Virtual Machines.

• Overseeing Engineering of Classical ML, Agentic and GenAI products, including:

• Disease Prediction and Patients Scoring over Structured and Unstructured Data

• Financial Forecasts

• Time-Series Big Data Anomaly Detection Systems

• Agentic and Generative Tools for Healthcare operations

• LLM-powered Summarization, Insights Extraction, Data Analysis

• Helping with the Team Growth, Hiring, and Continuous Learning culture.

• Gathering and Translating Clinical and Business Requirements to robust AI Solutions. A Tech Product Manager and Medical Experts will be assisting with the job.

Location and timezone:

• We are focused on hiring in time zones overlapping with the US (Portugal, Spain) or Western Europe

What about offer?

• Fully remote work.

• Interesting projects to work on.

• Opportunity to work with international team of first-class professionals.

• Unlimited PTO.

• Corporate hardware.

• Relocation assistance based on business needs.

• Team Building events.

Компания, занимающаяся разработкой облачных технологий для здравоохранения на базе искусственного интеллекта, ищет талантливого Senior AI Tech Lead для долгосрочного сотрудничества.

Требуемый опыт:

• Более 7 лет опыта в области прикладного машинного обучения и инженерии ИИ, в том числе более 3 лет в роли технического руководителя.

• Требуется опыт работы с Cloud-Native — не менее трех (3+) лет. Предпочтительно с GCP.

• Эта должность требует работы в Unix-подобной среде разработки.

• Глубокое знание фундаментальных дисциплин, таких как математика, статистика, машинное обучение, алгоритмы и структуры данных и т. д.

• Облачные решения (GCP) всегда имеют приоритет перед самохостинговыми, локальными или самодельными решениями на базе виртуальных машин.

• Знание управляемого искусственного интеллекта (например, Vertex AI) является большим преимуществом.

• Опыт работы с генеративным ИИ и LLM, такими как OpenAI, Gemini, Claude, Seedream, GPT Image, Veo3, Sora.

• Опыт работы с отраслевыми стандартами, т. е. PyTorch, Pandas, XGBoost, LightGBM, CatBoost, временными моделями, классификацией, архитектурой трансформеров, моделями SOTA и экосистемой Hugging Face.

• Владение решениями MLOps и DevOps, такими как Google Kubernetes Engine, Docker, Google Cloud Build.

• Хорошее знание Python и SQL.

• Глубокие знания в области архитектуры данных и внутреннего устройства баз данных, включая DDL, кластеризацию, разбиение на разделы, оптимизацию запросов и т. д.