arun.raja@dhira.ai Computer Vision Developer - ML Remote
arun.raja@dhira.ai Resume to ML ops Android Platform
• Experience of analyzing big data and preparing region wise visualizations for effective decision making. • Strong understanding of public policy to align data analytics with evidence-based decision making. arun.raja@dhira.ai
arun.raja@dhira.ai Job Title: Associate Machine Learning Scientist (Computer Vision Specialization) Associate Machine Learning (ML) computer vision AI development Senior Android Engineer (ML DevOps) Android-based ML deployments
Associate ML Scientist (Computer Vision) resume to arun.raja@dhira.ai Job Title: Associate Machine Learning Scientist (Computer Vision Specialization) Location: Open Type: Full-time Experience: 36 years Position Summary We are seeking an enthusiastic Associate Machine Learning (ML) Scientist with a specialization in computer vision applications to join our AI development team. The ideal candidate will have experience designing, developing, and deploying computer vision solutions to tackle real-world problems. This role involves working on high-impact AI applications and collaborating closely with data scientists, engineers, and domain experts to deliver reliable, scalable AI solutions. Key Responsibilities Develop robust, scalable machine learning models addressing complex computer vision challenges. Assist in the design and implementation of image and video analysis models, including object detection, face recognition, and image classification. Translate real-world challenges into well-defined AI/ML problem statements. Build and evaluate AI solutions through rigorous experimentation and performance benchmarking. Optimize and deploy computer vision models on various platforms, including edge and mobile devices. Curate, preprocess, and manage image and video datasets for model development and validation. Define and apply appropriate evaluation metrics for computer vision tasks, including accuracy, precision, recall, F1-score, and real-time inference benchmarks. Collaborate with cross-functional teams, including software engineers, product managers, and domain specialists, to deliver end-to-end AI solutions. Stay current with emerging research and best practices in computer vision, deep learning, and related fields. Requirements Educational Background: Bachelors, Master’s, or equivalent degree in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, Physics, or a related quantitative discipline. Experience: 3–6 years of professional experience in developing and deploying machine learning solutions, with a strong focus on computer vision applications. Technical Skills: Proficiency in Python and ML libraries such as TensorFlow, PyTorch, OpenCV, and scikit-learn. Solid understanding of deep learning architectures for image and video processing (e.g., CNNs, ResNet, MobileNet, EfficientNet). Familiarity with cloud platforms (AWS, Azure, GCP) and containerization tools (Docker) is preferred. Strong communication skills with the ability to translate technical concepts into actionable insights for cross-functional teams. Passion for applied machine learning and a willingness to adapt to diverse problem domains and real-world constraints. Preferred Qualifications Prior experience deploying computer vision models on mobile or edge devices. Experience working with face detection and recognition models. Familiarity with MLOps practices for model deployment, monitoring, and lifecycle management. Demonstrated ability to work independently and collaboratively in fast-paced, solution-driven environments. Exposure to ethical and responsible AI practices in computer vision applications.
Job Title: Senior Android Engineer (ML Ops) arun.raja@dhira.ai Job Title: Senior Android Engineer (ML Ops) Type: Full-time Experience Level: Mid to Senior (5 to 10 years) Position Summary We are seeking an experienced Senior Android Engineer (ML Ops) 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: Bachelors or Masters 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.