Lead Backend & MLOps Engineer

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Requirements:

• 8+ years of industry experience with modern systems development, ideally end to end pipelines and applications development
• Track record of shipping complex backend features end-to-end
• Ability to translate customer requirements into technical solutions
• Strong programming and computer science fundamentals and quality standards
• Experience with Python and modern web frameworks (FastAPI) and Pydantic
• Experience designing, implementing, and debugging web technologies and server architecture
• Experience with modern python packaging and distribution (uv, poetry)
• Deep understanding of distributed systems and scalable architecture
• Experience building reusable, modular systems that enable rapid development and easy modification
• Strong experience with data storage systems (PostgreSQL, Redis, BigQuery, MongoDB)
• Expertise with queuing/streaming systems (RabbitMQ, Kafka, SQS)
• Expertise with workflow orchestration frameworks (Celery, Temporal, Airflow) and DAG-based processing
• Proficiency in utilizing and maintaining cloud infrastructure services (Google Cloud/AWS/Azure)
• Experience with Kubernetes for container orchestration and deployment
• Solid grasp of system design patterns and tradeoffs
• Experience and in-depth understanding of AI/ML systems integration
• Deep understanding of the ML Lifecyle
• Experience with big data technologies and data pipeline development
• Experience containerizing and deploying ML applications (Docker) for training and inference workloads
• Experience with real-time streaming and batch processing systems for ML model workflows
• Experience with vector databases and search systems for similarity search and embeddings

Responsibilities:

• Partner closely with engineering (software, data, and machine learning), product, and design leadership to define product-led growth strategy with an ownership-driven approach
• Establish best practices, frameworks, and repeatable processes to measure the impact of every feature shipped, taking initiative to identify and solve problems proactively
• Make effective tradeoffs considering business priorities, user experience, and sustainable technical foundation with a startup mindset focused on rapid iteration and results
• Develop and lead team execution against both short-term and long-term roadmaps, demonstrating self-starter qualities and end-to-end accountability
• Mentor and grow team members to be successful contributors while fostering an ownership culture and entrepreneurial thinking
• Build and maintain backend systems and data pipelines for AI-based software platforms, integrating SQL/NoSQL databases and collaborating with engineering teams to enhance performance
• Design, deploy, and optimize cloud infrastructure on Google Cloud Platform, including Kubernetes clusters, virtual machines, and cost-effective scalable architecture
• Implement comprehensive MLOps workflows including model registry, deployment pipelines, monitoring systems for model drift, and CI/CD automation for ML-based backend services
• Establish robust testing, monitoring, and security frameworks including unit/stress testing, vulnerability assessments, and customer usage analytics
• Drive technical excellence through documentation, code reviews, standardized practices, and strategic technology stack recommendations