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Machine Learning Engineer

2 months ago

Gurugram, India Xebia Full time

Job Summary:

We are looking for an experienced Machine Learning Engineer with a strong background in algorithm development and deployment. The ideal candidate will have hands-on experience in designing, building, and deploying machine learning models in production environments. You will work closely with data scientists, software engineers, and product teams to integrate machine learning models into our products, ensuring they are scalable, efficient, and reliable.

Key Responsibilities:

Algorithm Development:

  • Design, develop, and optimize machine learning algorithms for various applications.
  • Implement and fine-tune models using techniques such as regression, classification, clustering, deep learning, and reinforcement learning.
  • Conduct exploratory data analysis and feature engineering to enhance model performance.
  • Collaborate with data scientists to translate business requirements into technical solutions.

Model Deployment:

  • Deploy machine learning models in production environments, ensuring they meet performance, scalability, and reliability requirements.
  • Utilize tools and platforms such as TensorFlow, PyTorch, Kubernetes, Docker, and AWS/GCP/Azure for model deployment and orchestration.
  • Implement CI/CD pipelines for automated deployment and monitoring of models.
  • Ensure models are continuously integrated and deployed with minimal disruption to production systems.

Performance Monitoring and Optimization:

  • Monitor the performance of deployed models and conduct regular evaluations to ensure they remain accurate and relevant.
  • Implement automated monitoring solutions to track model performance, data drift, and system health.
  • Optimize model inference times and resource usage to maintain efficiency in production.
  • Develop strategies for model versioning, rollback, and updates.

Collaboration and Communication:

  • Work closely with software engineering teams to ensure seamless integration of machine learning models into applications.
  • Collaborate with data engineering teams to ensure the availability of clean and well-structured data for model training and evaluation.
  • Communicate complex technical concepts to non-technical stakeholders in a clear and concise manner.

Research and Innovation:

  • Stay up-to-date with the latest advancements in machine learning, AI, and related technologies.
  • Experiment with and implement new tools, frameworks, and methodologies to improve model development and deployment processes.
  • Participate in code reviews, team discussions, and knowledge-sharing sessions to promote continuous learning and improvement.

Qualifications:

Education:

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. A PhD is a plus.

Experience:

  • 3-5 years of experience as a Machine Learning Engineer or in a similar role.
  • Proven experience in developing and deploying machine learning models in production environments.
  • Strong programming skills in Python, R, or similar languages.
  • Experience with machine learning frameworks like TensorFlow, PyTorch, Scikit-learn, or similar.

Skills:

  • In-depth knowledge of machine learning algorithms and techniques.
  • Experience with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).
  • Strong understanding of software development principles, including version control, CI/CD, and testing.
  • Proficiency in data processing and manipulation using tools like Pandas, NumPy, SQL, etc.
  • Excellent problem-solving skills and attention to detail.
  • Strong communication and teamwork abilities.

Key Competencies:

  • Ability to work independently and manage multiple projects simultaneously.
  • Strong analytical and critical thinking skills.
  • Adaptability to new technologies and a fast-paced environment.
  • Proactive attitude towards learning and implementing new methodologies.

Preferred Qualifications:

  • Experience with MLOps practices and tools.
  • Knowledge of big data technologies like Spark, Hadoop, or similar.
  • Familiarity with data privacy and security considerations in AI/ML applications.