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Senior Machine Learning Engineer

Anlage Infotech
Posted on
Anlage Infotech logo

Experience
10 - 16 yrs
Salary (CTC)
₹30L - ₹45L
Job Location
Bengaluru, India
Vacancy
1
Designation
Senior Machine Learning Engineer
Job Type
Not specified

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.

No Referrers Available

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