Senior Machine Learning Engineer

Anlage Infotech; Bengaluru; Hybrid; INR 3,500,000 - 7,000,000; Full time

Job summary

Anlage Infotech in Bengaluru is seeking a Senior Machine Learning Engineer with 10+ years of experience to lead ML initiatives across data processing, model development and production-grade solutions.

You will apply Python, PyTorch and Scikit-learn, design scalable ML systems, collaborate with data and software teams, and translate business needs into robust technical outcomes. Hybrid work flexibility available.

Qualifications

• Master's degree in Computer Science, ML, Data Science, Electrical Engineering or related field.
• 10+ years of professional experience in ML engineering or software engineering with ML focus.
• Strong Python proficiency and production-grade coding experience.
• Experience with PyTorch, Scikit-learn and ML frameworks.
• Solid knowledge of SQL/NoSQL databases and data processing.

Responsibilities

• Design, develop, implement, and maintain ML models and solutions for complex business problems.
• Apply ML algorithms with feature engineering, evaluation and optimization.
• Collaborate with data and software teams to deploy production-ready ML systems.
• Develop clean, scalable Python code and apply software engineering fundamentals.
• Work with large datasets; handle data processing, warehousing concepts and data quality.
• Communicate approaches and results clearly to technical and non-technical stakeholders.

Skills

Python

PyTorch

Scikit-learn

SQL

NoSQL

Distributed Systems

Education

Master's degree in Computer Science / ML / Data Science / related field

Tools

PyTorch

Scikit-learn

SQL

NoSQL

Job description

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

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.

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:
  • Data Structures
  • Algorithms
  • Object-Oriented Programming
  • Software Design Patterns
  • Distributed Systems
• Ensure solutions are scalable, reliable, maintainable, and efficient.

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.

ML Frameworks

• Develop machine learning solutions using frameworks and libraries including:
  • PyTorch
  • 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.

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.

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

Education

• Masters degree in:
  • Computer Science
  • Machine Learning
  • Data Science
  • Electrical Engineering
  • or another related quantitative field.

Experience

• 10+ years of professional experience in:
  • Machine Learning Engineering
  • Software Engineering with a strong ML focus
  • or a closely related role.

Programming

Must Have

• Strong proficiency in Python.
• Experience writing:
  • Production-grade code
  • Clean and maintainable code
  • Efficient code
  • Well-documented code

Good to Have

• Experience with additional programming languages such as:
  • Java
  • Go
  • C++

Software Engineering Fundamentals

Strong understanding of

• Software Design Patterns
• Data Structures
• Algorithms
• Object-Oriented Programming
• Distributed Systems
• Software Development Best Practices

Machine Learning

Strong theoretical and practical knowledge of

• Machine Learning Algorithms
• Supervised Learning
• Unsupervised Learning
• Feature Engineering
• Model Evaluation
• Evaluation Metrics
• Hyperparameter Tuning
• Model Optimization

ML Frameworks

• PyTorch
• Scikit-learn

Data

• Strong experience with SQL.
• Experience with NoSQL databases.
• Understanding of data warehousing concepts.
• 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.
• 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.
• Ability to work effectively in a fast-paced technical environment.