Machine Learning Engineer

Greetings from Maneva Job Description Job Title - Machine Learning Engineer Experience - 5 -15 days Location - Bangalore ( Kodathi) Notice - Immediate to 60 days Job Summary: We are seeking a highly skilled and versatile  Senior Machine Learning Engineer  who embodies the rare combination of a strong software engineer, a pragmatic data scientist, and an expert in building robust, scalable ML applications. This role is critical to our mission, bridging the gap between cutting-edge ML research and robust, production-ready systems. Candidate will be instrumental in designing, developing, deploying, and maintaining our core AI-powered products and features. This demands a blend of analytical rigor, coding prowess, architectural foresight, and a deep understanding of the entire machine learning lifecycle, from data exploration and model development to deployment, monitoring, and continuous improvement. Key Responsibilities: End-to-End ML Application Development:  Lead the design, development, and deployment of machine learning models and intelligent systems into production environments, ensuring they are robust, scalable, and performant. Software Design & Architecture:  Apply strong software engineering principles to design and build clean, modular, testable, and maintainable ML pipelines, APIs, and services. Contribute significantly to the architectural decisions for our ML platform and applications. ML Model Development & Optimization:  Collaborate with Data Scientists to understand business problems, explore data, develop, train, and evaluate machine learning models (e.g., supervised, unsupervised, deep learning, reinforcement learning). Optimize models for performance, efficiency, and interpretability. Data Engineering for ML:  Design and implement data pipelines for feature engineering, data transformation, and data versioning to support ML model training and inference. MLOps & Productionization:  Establish and implement best practices for MLOps, including CI/CD for ML, automated testing, model versioning, monitoring (performance, drift, bias), and alerting systems for production ML models. Performance & Scalability:  Identify and resolve performance bottlenecks in ML systems. Ensure the scalability and reliability of deployed models under varying load conditions. Collaboration & Mentorship:  Work closely with cross-functional teams including Data Scientists, Software Engineers, Product Managers, and DevOps to integrate ML solutions seamlessly into our products. Potentially mentor junior engineers on best practices in ML engineering and software design. Research & Innovation:  Stay abreast of the latest advancements in machine learning, MLOps, and related technologies. Propose and experiment with new techniques and tools to improve our ML capabilities. Documentation:  Create clear and comprehensive documentation for ML models, pipelines, and services. Required Qualifications: Education:  Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Electrical Engineering, or a related quantitative field. Experience:  5+ years of professional experience in Machine Learning Engineering, Software Engineering with a strong ML focus, or a similar role. Exceptional Programming Skills:  Expert-level proficiency in Python, including experience with writing production-grade, clean, efficient, and well-documented code. Experience with other languages (e.g., Java, Go, C++) is a plus. Strong Software Engineering Fundamentals:  Deep understanding of software design patterns, data structures, algorithms, object-oriented programming, and distributed systems. Machine Learning Expertise: Solid theoretical and practical understanding of various machine learning algorithms Proficiency with ML frameworks such as PyTorch, Scikit-learn. Experience with feature engineering, model evaluation metrics, and hyperparameter tuning. Data Handling:  Experience with SQL and NoSQL databases, data warehousing concepts, and processing large datasets. Problem-Solving:  Excellent analytical and problem-solving skills, with a pragmatic approach to delivering solutions. Communication:  Strong verbal and written communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences. Preferred Qualifications: Master's or Ph.D. in a relevant field. Experience with big data technologies (e.g., Spark, Hadoop, Kafka). Contributions to open-source projects or a strong portfolio of personal projects. Experience with A/B testing and experimental design for ML models. Knowledge of data governance, privacy, and security best practices in ML. If you are excited to grab this opportunity, please apply directly or share your CV at   and