
Position Overview: The Lead Software Architect - Python & Data Engineering is a senior technical leadership role responsible for designing and owning end-to-end architecture for data-intensive, AI/ML, and analytics platforms, while mentoring developers and ensuring technical excellence across the organization.
Key Responsibilities:
- Design end-to-end software architecture for data-intensive applications, AI/ML pipelines, and analytics platforms
- Evaluate trade-offs between competing technical approaches
- Define data models, API approach, and integration patterns across systems
- Create technical specifications and architecture documentation
- Lead by example through production-grade Python code and mentor developers on engineering fundamentals
- Conduct design and code reviews focused on architectural soundness
- Establish engineering standards, coding practices, and design patterns for the team
- Translate business requirements into technical architecture
- Collaborate with data scientists, analysts, and other teams to design integrated solutions
- Whiteboard and defend system design and architectural choices
- Take responsibility for system performance, reliability, and maintainability
- Identify and resolve architectural bottlenecks proactively
Required Skills:
- 8+ years of experience in software architecture and development
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- Strong foundations in data structures, algorithms, and computational complexity
- Experience in system design for scale, including caching strategies, load balancing, and asynchronous processing
- 6+ years of Python development experience
- Deep knowledge of Django, Flask, or FastAPI
- Expert understanding of Python internals including GIL and memory management
- Experience with RESTful API design and event-driven architectures (Kafka, RabbitMQ)
- Proficiency in data processing frameworks such as Pandas, Apache Spark, and Airflow
- Strong SQL optimization and database design experience (PostgreSQL, MySQL, MongoDB) Experience with AWS, GCP, or Azure cloud platforms
- Knowledge of containerization (Docker) and orchestration (Kubernetes)
- Hands-on experience designing CI/CD pipelines Preferred (Bonus)
Skills:
- Experience deploying ML models to production (MLOps, model serving, monitoring) Understanding of ML system design including feature stores and model versioning
- Familiarity with ML frameworks such as scikit-learn, TensorFlow, and PyTorch
- Open-source contributions or technical blogging demonstrating architectural depth
- Experience with modern front-end frameworks for full-stack perspective

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