Job Description:
We are seeking an experienced Senior Android Engineer (ML DevOps) with a strong background in Android-based ML deployments to lead the integration and scaling of machine learning solutions across diverse domains. The ideal candidate will have hands-on experience deploying computer vision models, particularly ResNet and dlib, on Android devices. This role involves end-to-end ownership of ML pipelines, from model optimization and deployment to monitoring, asset management, and ensuring seamless production performance on Android platforms.
Key Responsibilities
ML Infrastructure & MLOps
? Design, implement, and manage scalable ML infrastructure to support diverse projects.
? Develop and maintain MLOps pipelines for continuous integration, delivery, and monitoring of ML models.
? Track and optimize model performance, implementing strategies for real-time improvements and scalability.
? Ensure robust version control, reproducibility, and governance for both ML models and datasets.
Android ML Deployment
? Implement model serving and asset management techniques (e.g., Android Asset Packs) to deploy ResNet and dlib-based models efficiently within Android applications.
? Collaborate with Android developers to integrate ML models into production apps with optimal inference performance.
? Develop and implement edge deployment strategies for achieving low-latency, high-accuracy performance on Android devices.
Collaboration & Stakeholder Engagement
? Work closely with data scientists, ML engineers, mobile developers, and product teams to align ML solutions with project objectives.
? Engage with stakeholders to gather requirements, provide technical recommendations, and deliver impactful ML-driven features.
? Contribute to internal knowledge-sharing, code reviews, and best practice improvements within the ML engineering team.
Required Qualifications
? Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
? Experience: 5–10 years of experience in developing and deploying machine learning models in production, including Android-based applications.
Technical Skills:
? Proficiency in Python, Java, and ML libraries such as TensorFlow, PyTorch, OpenCV, and scikit-learn.
? Experience with MLOps tools, CI/CD platforms, and frameworks for model deployment, monitoring, and lifecycle management.
? Strong understanding of cloud services (AWS, Azure, GCP), containerization (Docker, Kubernetes), and mobile app integration workflows.
? Familiarity with feature store frameworks, data versioning tools, and model asset management strategies.
? Hands-on experience deploying ML models on Android, with proven skills in optimizing for device constraints and performance.
Preferred Qualifications
? Experience with edge ML deployment techniques and tools.
? Familiarity with data privacy, security, and compliance frameworks for AI deployments.
? Excellent problem-solving, debugging, and performance tuning skills.
? Strong verbal and written communication, with the ability to explain complex ML concepts to diverse audiences.
? Demonstrated ability to work both independently and collaboratively in fast-paced, outcome-driven environments.