SDE 2 - ML

We're looking for an ML Engineer to build and own the systems that bring machine learning models into production. You'll work at the intersection of software engineering and ML, designing microservices, building APIs, optimizing for performance, and ensuring our models run reliably at scale. This is a hands-on engineering role. You'll spend most of your time writing production Python, building and maintaining APIs, making architectural decisions, and collaborating closely with data scientists and product teams to ship ML-powered features.

Responsibilities:

• Own the end-to-end lifecycle of ML microservices from design through deployment and monitoring.
• Build and maintain APIs that serve ML models to internal and external consumers.
• Partner with data scientists and product teams to translate business requirements into technical solutions.
• Write clean, efficient, and maintainable code that others can build on.
• Take ownership of and improve our existing codebases, systems, and workflows.
• Communicate technical concepts clearly to both engineers and non-technical stakeholders.

Requirements:

• Bachelor's degree in Computer Science or equivalent.
• 3-5+ years of backend engineering experience, with at least 3 years in Python.
• Strong experience building APIs with Flask or FastAPI.
• Solid experience with databases and SQL (PostgreSQL, MySQL, or similar).
• Experience with cloud platforms (AWS/GCP).
• Hands-on experience with message queues (RabbitMQ, Kafka, or SQS), Docker, and CI/CD pipelines.
• Familiarity with monitoring tools (Datadog, Grafana) for tracking latency, errors, and alerts.
• Experience deploying ML models in production.
• Clear communication and collaboration skills.

Nice to Have:

• Startup experience.
• Fintech background.

Technical Skills:

• Languages and Frameworks: Python, Flask/FastAPI.
• APIs: API design and development.
• Databases: SQL, PostgreSQL, MySQL.
• Cloud and Infrastructure: AWS/GCP, Docker, CI/CD.
• Event Streaming and Queues: RabbitMQ, Kafka, SQS.
• Observability and Monitoring: Datadog, Grafana.
• Core Competencies: System design, ML deployment.