Senior Python Developer (ML Platform)

We are looking for a Senior Python Engineer to join our ML Platform team. You will be responsible for building the backend systems and infrastructure that power machine learning across a global fintech project.

This is a platform engineering role focused on building scalable, reliable, and secure systems that enable ML engineers and data scientists to develop, deploy, and operate models in production. Your work will directly support low-latency, real-time environments, such as fraud detection.

Please note: Prior Machine Learning experience is NOT required! If you are a strong backend engineer with an interest in data-driven platforms and ML systems, this is the perfect role for you.

Responsibilities:

• Backend Development: Design and build production-grade Python services (FastAPI / microservices) that support the ML lifecycle, including feature computation, model inference, and monitoring.

• Real-Time Data Pipelines: Develop and operate data pipelines to support low-latency, high-availability use cases (like fraud detection) using event-driven architectures (Kafka, Flink).

• Platform Engineering: Build capabilities to enable online and offline feature stores, working closely with Data Engineering to ensure reliable data flows.

• Developer Experience (DevEx): Improve the daily lives of ML engineers and data scientists through better tooling, automation, and self-service APIs.

• Cloud & DevOps: Build and maintain CI/CD pipelines (GitOps, GitHub Actions, ArgoCD) and ensure the observability (Datadog), resilience, and scalability of platform services.

• Security & Compliance: Collaborate with Infrastructure and Security teams to ensure all systems meet strict financial and regulatory standards.

Min requirements:

• Python Mastery: Strong commercial experience in building production-grade backend systems and microservices in Python (this is not a scripting role).

• Distributed Systems: Proven experience designing, building, and operating backend services or distributed systems.

• Cloud & Containerization: Solid practical experience with AWS (EKS, S3, Lambda) and container orchestration (Docker, Kubernetes).

• Data Systems: Experience working with databases, data pipelines, or streaming platforms.

• Engineering Best Practices: Solid understanding of testing methodologies, CI/CD, and code quality standards.

• High-Availability Focus: Ability to work in highly regulated, high-availability environments where system resilience is critical.

Would be a plus:

• Experience with real-time streaming technologies (Kafka, Flink) and low-latency systems.

• Background in fintech, payments, risk systems, or fraud detection.

• Experience with ML platforms/tools (e.g., SageMaker, Ray, Tecton) or exposure to feature stores (online/offline architectures).

• A general understanding of the machine learning lifecycle (training, inference, feature engineering).

We offer:

• Opportunity to work on bleeding-edge projects

• Work with a highly motivated and dedicated team

• Competitive salary

• Flexible schedule

• Benefits package - medical insurance, sports

• Corporate social events

• Professional development opportunities

• Well-equipped office

About us:

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.