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

Team Size: 15 Data Scientists, ML Engineers, Data Engineers, Data Analysts, and Medical Experts.

Stealth-mode AI-powered Cloud-Native Health-Tech company. The Business Domain revolves around providing Better Care for the US Patients; meaning, it’s about Treating Patients well. The company was founded more than five years ago.

• 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.

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
• Vertex AI
• LLMs and GenAI
• Agent Development Kit (ADK)
• GSuite
• LucidChart
• Slack
• Jira

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

AI & Leadership

• 7+ years of Experience in Applied ML and AI engineering, including 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.
• Fluent Russian is required for effective collaboration with Russian-speaking teammates.

Cloud-Native & GCP

• Cloud-Native Experience – at least 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.

Paid Time Off

The company has Unlimited PTOs Policy and compensated New Years Holidays on top of that. The misuse of the policy isn’t welcomed, though it’s definitely possible to take at least two weeks – and fully compensated – vacation, or more.

Corporate Hardware

The company provides Corporate Hardware for employees who completed their Probationary Period, as well as proven their value. Means of Communication We use Google Workspace, Slack, Zoom, and similar collaboration tools for messaging, meetings, and document sharing across the organization. We don’t use Microsoft Outlook, Microsoft Team and similar software.

Cloud-Native

There’s real ability to make most solutions in a Cloud-Native and Third-Party Integrations manner, rarely spinning something self-hosted (e.g., over GKE). Fully Serverless Approach, based on Cloud Functions and Cloud Run is definitely not welcomed.

Relocation

Relocation assistance to a desired country may be provided after the probationary period, based on business needs and demonstrated performance.

Team Building

The company partially compensates Team Building events, when multiple teammates are located in nearby countries.