Senior Machine Learning Engineer

Experience: 10+ Years

Location: Bangalore

Employment Type: Full-Time

Work Mode: Hybrid

Job Summary:

We are looking for an experienced Senior Machine Learning Engineer with 10+ years of professional experience in Machine Learning Engineering, Software Engineering with a strong ML focus, or a similar role.

The ideal candidate will combine strong software engineering fundamentals with deep expertise in machine learning, data processing, model development, and production-grade ML solutions. The role requires hands-on experience with Python, PyTorch, Scikit-learn, ML algorithms, feature engineering, model evaluation, hyperparameter tuning, SQL/NoSQL databases, and large-scale data processing.

The successful candidate should be able to translate complex business and technical problems into practical, scalable ML solutions and communicate effectively with both technical and non-technical stakeholders.

Key Responsibilities:

1. Machine Learning Engineering:

- Design, develop, implement, and maintain machine learning models and solutions for complex business problems.

- Apply strong theoretical and practical knowledge of machine learning algorithms to real-world use cases.

- Select appropriate algorithms, approaches, and techniques based on business and technical requirements.

- Perform feature engineering, model training, validation, evaluation, and optimization.

- Define and analyze appropriate model evaluation metrics.

- Perform hyperparameter tuning and model optimization.

- Collaborate with engineering and data teams to transition ML models into production-ready solutions.

2. Software Engineering:

- Develop clean, efficient, scalable, production-grade, and well-documented Python code.

- Apply strong software engineering principles to ML systems and applications.

- Design reusable and maintainable software components using appropriate design patterns and object-oriented programming principles.

- Apply strong knowledge of:

1. Data Structures

2. Algorithms

3. Object-Oriented Programming

4. Software Design Patterns

5. Distributed Systems

- Ensure solutions are scalable, reliable, maintainable, and efficient.

3. Data Engineering & Data Handling:

- Work with large and complex datasets to support machine learning initiatives.

- Develop data processing and transformation workflows required for ML model development.

- Work with both SQL and NoSQL databases.

- Apply knowledge of data warehousing concepts and large-scale data processing.

- Perform data analysis, preparation, validation, and feature extraction.

- Collaborate with data engineering teams to ensure availability and quality of data required for ML applications.

4. ML Frameworks:

- Develop machine learning solutions using frameworks and libraries including PyTorch and Scikit-learn.

- Evaluate and select appropriate frameworks and techniques based on the requirements of the solution.

- Maintain and optimize ML implementations for performance and scalability.

5. Problem Solving & Innovation:

- Analyze complex technical and business problems and develop pragmatic, scalable solutions.

- Evaluate different approaches and make data-driven technical decisions.

- Identify opportunities to improve existing ML models, processes, and engineering practices.

- Troubleshoot model, data, and application-related issues.

- Balance technical excellence with practical business requirements and delivery timelines.

6. Collaboration & Communication:

- Work closely with Data Scientists, ML Engineers, Software Engineers, Data Engineers, Product Managers, and other stakeholders.

- Translate business requirements into technical and ML solutions.

- Explain complex machine learning and technical concepts clearly to technical and non-technical audiences.

- Prepare technical documentation and communicate solution approaches, assumptions, limitations, and outcomes.

- Collaborate effectively across teams to deliver high-quality solutions.

Required Qualifications:

1. Education:

- Masters degree in:

1. Computer Science

2. Machine Learning

3. Data Science

4. Electrical Engineering

5. Or another related quantitative field.

2. Experience:

- 10+ years of professional experience in:

1. Machine Learning Engineering

2. Software Engineering with a strong ML focus

3. Or a closely related role.

3. Programming:

- Must Have: Strong proficiency in Python and experience writing production-grade, clean, maintainable, efficient, and well-documented code.

- Good to Have: Experience with additional programming languages such as Java, Go, or C++.

4. Software Engineering Fundamentals:

- Strong understanding of Software Design Patterns, Data Structures, Algorithms, Object-Oriented Programming, Distributed Systems, and Software Development Best Practices.

5. Machine Learning:

- Strong theoretical and practical knowledge of Machine Learning Algorithms, Supervised Learning, Unsupervised Learning, Feature Engineering, Model Evaluation, Evaluation Metrics, Hyperparameter Tuning, and Model Optimization.

6. ML Frameworks:

- PyTorch and Scikit-learn.

7. Data:

- Strong experience with SQL, experience with NoSQL databases, understanding of data warehousing concepts, and experience processing and working with large datasets.

Good-to-Have Skills:

- Java, Go, C++, experience with distributed ML systems, experience taking ML models from development through production, exposure to ML pipelines and production ML environments, experience with cloud-based ML platforms, and knowledge of MLOps practices.

Key Competencies:

- Strong analytical and problem-solving skills, pragmatic approach to solving complex problems, strong software engineering mindset, ability to work independently as well as collaboratively, strong verbal and written communication, ability to explain complex technical concepts to diverse audiences, strong ownership and execution capabilities, and ability to work effectively in a fast-paced technical environment.