Key responsibilities

• Architect, implement, and maintain ML-backed services and APIs (Python) with clear interfaces, tests, and observability.
• Build near-real-time computer vision pipelines meeting defined latency/throughput SLOs.
• Fine-tune/evaluate open-source models and optimize inference (e.g., batching, quantization, ONNX/TensorRT where appropriate).
• Collaborate on data contracts and schemas; ensure reproducible experiments and traceable releases.
• Own service reliability in production (monitoring, incident response, root-cause fixes).
• Document designs and decisions; conduct code reviews and mentor juniors.

Minimum qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, or related field.
• 3–6 years industry experience delivering ML solutions in production using Python.
• Strong software engineering fundamentals (testing, packaging, type hints, CI).
• Hands-on with either real-time computer vision pipelines or LLM/RAG services, using open-source tooling.
• Experience serving models behind REST/gRPC and operating containerized services (Docker) on cloud or on-prem.
• Practical performance tuning (profiling, memory/latency trade-offs).