Machine Learning Engineer

This is a compelling opportunity for a Machine Learning Engineer to join an innovative entity in the insurance industry based in Hyderabad, India. As a Machine Learning Engineer with 3 to 5 years of experience, you will play a critical role in designing, developing, and deploying machine learning models and systems that drive business value. Working closely with data scientists, software engineers, and product teams, you will be responsible for building scalable and efficient machine learning pipelines, optimizing model performance, and integrating models into production environments. Key Responsibilities: - Model Development & Training: Develop and train machine learning models, including supervised, unsupervised, and deep learning algorithms, to tackle business problems. - Data Preparation: Collaborate with data engineers to clean, preprocess, and transform raw data into usable formats for model training and evaluation. - Model Deployment & Monitoring: Deploy machine learning models into production environments, ensuring seamless integration and monitoring model performance. - Feature Engineering: Create and test new features to enhance model performance and optimize feature selection to reduce complexity. - Algorithm Optimization: Research and implement cutting-edge algorithms to improve model accuracy, efficiency, and scalability. - Collaborative Development: Work closely with stakeholders to understand business requirements, develop ML models, and integrate them into products. - Model Evaluation: Conduct thorough evaluations using statistical tests, cross-validation, and A/B testing to ensure reliability. - Documentation & Reporting: Maintain comprehensive documentation of processes, models, and systems, providing insights and recommendations. - Code Reviews & Best Practices: Participate in peer code reviews, ensuring adherence to coding best practices and continuous improvement. - Stay Updated on Industry Trends: Keep abreast of new techniques and advancements in machine learning, suggesting improvements for internal processes. Required Skills & Qualifications: - Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or related field. - Experience: 3 to 5 years in a similar role. - Programming Languages: Proficiency in Python (preferred), R, or Java. Experience with ML libraries like TensorFlow, PyTorch, Scikit-learn, and Keras. - Data Manipulation: Strong SQL knowledge and experience with tools like Pandas, NumPy, Spark for large datasets. - Cloud Services: Experience with AWS, Google Cloud, or Azure, especially ML services like SageMaker or AI Platform. - Model Deployment: Hands-on experience deploying ML models using Docker, Kubernetes, and CI/CD pipelines. - Problem-Solving Skills: Strong analytical skills to understand complex data problems and implement effective solutions. - Mathematics and Statistics: Solid foundation in mathematical concepts related to ML. - Communication Skills: Strong verbal and written communication skills to collaborate effectively with teams and stakeholders. Preferred Qualifications: - Experience with deep learning frameworks, NLP, computer vision, or recommendation systems. - Familiarity with version control systems, collaborative workflows, model interpretability, fairness techniques. - Exposure to big data tools like Hadoop, Spark, Kafka. Screening Criteria: - Bachelors or Masters degree in relevant fields. - 3 to 5 years of hands-on experience in a related role. - Proficiency in Python. - Experience with key ML libraries and tools. - Strong SQL knowledge. - Experience with cloud platforms and ML services. - Hands-on experience deploying ML models. - Solid foundation in mathematical concepts. - Available to work from office in Hyderabad. - Available to join within 30 days. Considerations: - Location: Hyderabad. - Working pattern: 5 days in the office. Evaluation Process: - Round 1: HR Round. - Rounds 2 & 3: Technical Rounds. - Round 4: Discussion with CEO. Interested Profiles, kindly apply. Additional inputs will be gathered from candidates during the application process.,