Senior Software Engineer - Python

📋 Description

• • Architect and own the technical roadmap for a unified ML platform that stitches together computer-vision model evaluation tools, data pipelines, and user-facing dashboards into one coherent experience.
• • Design, build, and harden Python-based micro-services and REST/GraphQL APIs that expose clean, versioned endpoints for data ingestion, model testing, metrics calculation, and real-time alerting.
• • Lead integration efforts across heterogeneous AI/ML libraries (PyTorch, TensorFlow, ONNX, Hugging Face, OpenCV) while enforcing consistent data contracts, error handling, and observability standards.
• • Implement scalable orchestration layers using Kubernetes, Docker, and CI/CD pipelines so that every commit is automatically tested, containerized, and deployed to staging and production environments.
• • Create intuitive web interfaces (React + FastAPI backend) that allow researchers, product managers, and external partners to upload datasets, launch evaluation jobs, and visualize results without writing code.
• • Optimize inference latency and memory footprint by profiling GPU/CPU usage, leveraging quantization, pruning, and batching strategies, and publishing performance benchmarks to internal stakeholders.
• • Establish rigorous test coverage (unit, integration, chaos) and code-quality gates (mypy, black, flake8, pytest) so the platform remains reliable as it scales to thousands of concurrent evaluation runs.
• • Mentor mid-level and junior engineers through pair programming, architecture reviews, and weekly tech talks; champion best practices in Python, distributed systems, and MLOps.
• • Collaborate daily with product, design, and open-source community contributors to translate research prototypes into hardened, customer-ready features while maintaining backward compatibility.
• • Contribute 3 % of engineering time to upstream open-source projects, filing PRs, triaging issues, and representing OpenTeams at conferences and meetups to strengthen our ecosystem.
• • Monitor cost and energy efficiency of cloud workloads; implement auto-scaling, spot-instance scheduling, and carbon-aware job routing to align with our sustainability goals.
• • Own on-call rotation for the ML platform, responding to incidents with blameless post-mortems and driving systemic fixes that prevent recurrence.
• • Translate complex technical trade-offs into clear narratives for non-technical stakeholders, ensuring alignment between engineering decisions and business outcomes.
• • Champion security and privacy by design: encrypt data at rest and in transit, enforce RBAC, and maintain SOC 2 and GDPR compliance across all services.