ADA - Lead Machine Learning Engineer - Python/Tensorflow

<p><p><b>Job Title :</b> Lead ML Engineer<br/><br/><b>Location :</b> Bangalore, Karnataka<br/><br/><b>Company :</b> ADA Global<br/><br/><b>Job Description :</b></p><p><br/>We are seeking a Lead Machine Learning Engineer to drive the development and deployment of cutting-edge ML systems.<br/></p><p><br/></p><p>You will lead a team of ML engineers, collaborate with cross-functional teams, and ensure the scalability, efficiency, and reliability of our ML solutions.<br/></p><p><br/></p><p>This role requires strong technical expertise, leadership, and a deep understanding of ML systems in production.<br/><br/><b>Key Responsibilities :</b></p><p><br/>- End-to-End ML Development Design, build, and deploy scalable ML models for real-world applications.<br/><br/>- Team Leadership Guide and mentor a team of ML engineers and data scientists.<br/><br/>- Model Optimization & MLOps Implement model monitoring, retraining, and CI/CD pipelines for ML models.<br/><br/>- Big Data & Cloud Integration Work with large-scale data pipelines, distributed computing, and cloud services (AWS/GCP/Azure).<br/><br/>- Cross-Functional Collaboration Work closely with data engineers, product teams, and software developers.<br/><br/>- Algorithm Innovation Research and implement state-of-the-art ML and deep learning techniques.<br/><br/>- Performance & Scalability Ensure ML models are efficient and optimized for deployment in production environments.<br/><br/><b>Requirements :</b></p><p><br/>- <b>Education :</b> Bachelor's or masters in computer science, AI, Data Science, or a related field.<br/><br/>- <b>ML Expertise :</b> Strong background in Supervised, Unsupervised, and Deep Learning techniques.<br/><br/>- <b>Tech Stack :</b> Python, TensorFlow, PyTorch, Scikit-learn, Spark, Kubernetes, Docker.<br/><br/>- <b>Data & Infrastructure :</b> Experience with Big Data (Hadoop, Snowflake), Cloud ML tools (SageMaker, Vertex AI).<br/><br/>- <b>MLOps & CI/CD :</b> Hands-on with ML lifecycle management, monitoring, and automation.<br/><br/>- <b>APIs & Deployment :</b> Experience in deploying ML models via REST APIs and microservices</p><br/></p> (ref:hirist.tech)