ML Engineer

Job Description

Job Objective: As an ML Engineering Intern, your primary role will be to deploy, monitor, and scale machine learning models, including object detection, human detection, facial recognition, automatic number plate recognition, red light violation detection, suspicious activity detection, etc., for our AI-based surveillance system. You will ensure these models perform optimally on both cloud and embedded edge devices in real-world environments. Key Responsibilities: Collaborate with ML developers to understand model requirements and deployment objectives. Implement robust deployment pipelines for machine learning models on cloud platforms and embedded edge devices. Monitor model performance in production, identifying bottlenecks and proposing solutions to improve scalability and efficiency. Work with cross-functional teams to integrate ML models into the existing infrastructure and ensure seamless operation. Document deployment processes and create monitoring dashboards for ongoing performance tracking. Qualifications and Skills: Currently pursuing or recently graduated with a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Solid understanding of Machine Learning / Computer Vision & Image Processing concepts and algorithms, with a focus on deployment and scalability. Strong programming skills in C++ & Python, with proficiency in using deployment frameworks such as TensorFlow Serving, TorchServe, or ONNX. Experience with containerization technologies like Docker and orchestration tools like Kubernetes. Knowledge of cloud services (AWS, GCP, Azure) and familiarity with IoT devices or edge computing. Strong problem-solving skills and ability to work collaboratively in a fast-paced environment. Compensation (Yearly): ₹4.2 lacs (incl. performance bonus) Expectations: Commitment to work at least 9 hours daily (Monday-Saturday). Proactive and enthusiastic about implementing and scaling machine learning solutions. Meticulous attention to detail and commitment to high-quality, reliable deployment practices. Quick adaptation to new technologies and continuous improvement in methodologies. Professionalism and integrity in all work and interactions.