About BayRock Labs
At BayRock Labs, we pioneer innovative tech solutions that drive business transformation. As a leading product engineering firm based in Silicon Valley, we provide full-cycle product development, leveraging cutting-edge technologies in AI, ML, and data analytics. Our collaborative, inclusive culture fosters professional growth and work-life balance. Join us to work on ground-breaking projects and be part of a team that values excellence, integrity, and innovation. Together, let's redefine what's possible in technology.
We are seeking a highly experienced and technically adept Lead Python Engineer to architect, design, and implement the core microservices and workflow systems that power our next-generation, AI-driven applications. The ideal candidate has over eight years of hands-on experience building high-scale, production-grade backends using Python and modern asynchronous frameworks. This role requires a strong focus on system reliability, code quality, rigorous testing, and best practices in a microservices environment, particularly with deep experience in data integration using Snowflake.
Key Responsibilities
- Architectural Design & Implementation
Microservices: Lead the design and implementation of robust, scalable, and secure microservices using Python and FastAPI (or similar asynchronous frameworks).
API Specification/AI Native Development: Define, document, and maintain all service endpoints using the OpenAPI Specification (OAS) standard to ensure consistency, clarity, and ease of consumption by frontend and internal teams.
Workflow Systems: Design and implement resilient, step-based state machines or declarative Workflow Systems to manage complex, multi-step business logic and data processing pipelines.
- AI Enablement & Data Integration
AI-Driven Development: Integrate machine learning models (AI/ML) into production microservices.
Data Integration: Architect and develop robust data synchronization with experience in Snowflake integrations (e.g., using Snowflake Python Connector or ORM extensions) to handle large-scale analytical and transactional data flows.
Data Persistence: Implement and optimize data models and persistence layers using modern Database ORMs (e.g., SQLAlchemy, SQLModel) for relational and NoSQL databases.
- Engineering Quality & Best Practices
Code Quality: Define and enforce organization-wide Code Base Best Practices and rigorous Code Quality standards, including performance, security, and maintainability.
Testing: Establish comprehensive Unit Test Cases, integration tests, and end-to-end testing strategies to ensure high coverage and reliability across all services.
Mentorship: Provide technical leadership, mentorship, and code reviews to mid and senior-level engineers, fostering a culture of technical excellence.
Required Qualifications
Experience: 8+ years of professional software development experience, primarily focused on backend systems using Python.
Core Backend: Expert proficiency in Python and developing high-performance RESTful APIs, with specific, demonstrable experience using FastAPI.
API Standards: Deep understanding and practical experience defining APIs using the OpenAPI Specification (OAS), including tooling for validation and documentation.
Architecture: Proven history of designing, deploying, and maintaining systems built on Microservice Architectures.
Data Layer: Expertise with relational databases and using advanced Database ORM features for complex query optimization and schema migrations.
Workflow Systems: Experience defining, implementing, and managing complex business logic using step-based, state-driven, or declarative workflow systems (e.g., orchestration tools, custom state machines).
Cloud & Data: Strong experience working with cloud environments (AWS, GCP, or Azure) and significant experience integrating systems with Snowflake.
Preferred Qualifications (Nice-to-Haves)
Experience in integrating AI-Driven Development principles into software lifecycles.
Experience with AI Editors (Cursor, Windsurf etc) for coding
Understanding on RAG and AI applications
Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.
Knowledge of asynchronous programming patterns and performance tuning in Python (asyncio).
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