This is a key position that bridges the gap between data science and production engineering, ensuring high performance, reliability, and maintainability of our ML-powered products.
Responsibilities:
Collaborate with data scientists to understand modeling outputs and convert them into deployable services.
Design and develop robust, scalable backend systems and microservices to support AI use cases.
Own the deployment and monitoring of ML models in production (with CI/CD, logging, observability).
Implement data processing pipelines in support of model training and inference.
Ensure software adheres to best practices in architecture, testing, and documentation.
Optimize model inference for latency, throughput, and resource efficiency.
Contribute to design decisions and technical strategy alongside AI and infrastructure leads.
Requirements:
5+ years of experience as a backend/software engineer, preferably in Python, Go, or Java.
Strong experience with designing APIs, building microservices, and integrating third-party services.
Familiarity with ML workflows: model serving, feature extraction, and batch vs real-time inference.
strong architectural/design skills, including working with message queues like Kafka, relational and NoSQL databases, and distributed systems.
Experience deploying services in containerized environments (e.g., Docker, Kubernetes).
Proficient with cloud-native tools or on-prem equivalents (e.g., logging, tracing, metrics).
Knowledge of data processing frameworks (e.g., Pandas, Spark, Airflow) is a plus.
Comfortable reading and working with Python-based ML code (scikit-learn, TensorFlow, PyTorch, etc.).
Strong ownership mindset and a collaborative attitude.
Nice to Have:
Experience with model versioning and ML serving frameworks (e.g., MLflow, Seldon, Triton).
Understanding of data privacy/security implications in model and data pipelines.
Experience working in cross-functional teams with data scientists and product owners.
