What You'll Do
Design and develop a full ML platform for the computer vision lifecycle: data collection, training, evaluation, observability, and finally implement the final product in the production app.
Develop and optimize ML models for deployment in Embedded and Real-Time edge machines.
Help build Agentic process automation to improve R D development work across all teams, including tool integration, orchestration, auditability, and integration into CI/CD pipelines.
Contribute to architecture decisions, code quality standards, and engineering best practices across the team.
Continuously research and push for the latest technology in the AI world and integrate it into the product development lifecycle.
Collaborate closely with software, hardware, QA, and product teams to embed vision capabilities into end-to-end automation workflows.
Requirements: B.A./B.Sc. in Computer Science, Electrical Engineering, or a related technical field.
3-6 years of hands-on experience in Machine Learning engineering, data engineering, or MLOps, building production-grade solutions.
Strong proficiency in Python and CV/ML frameworks (PyTorch, Hugging Face, and LangChain)
Experience integrating vision or ML models into Real-Time systems using serving methods such as Triton Inference Server / Ray Serve, with High-performance API's using FASTAPI (HTTP) and gRPC.
Experience with AWS services (or other cloud infrastructure) - SageMaker, S3, EC2, and familiarity with ECS and EKS.
Experience with Kubernetes and containerized deployments. (Advantage)
Experience in MLOps platform building and operating production ML systems (advantage)
Ability to work both independently and collaboratively within a multidisciplinary team.
This position is open to all candidates.