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Machine Learning Engineer Leader
3 weeks ago
As a Senior ML Ops Engineer at GlobalLogic Inc, you will be responsible for designing and developing cutting-edge machine learning models and algorithms that drive innovation in our products. You will work closely with data scientists, software engineers, and product teams to ensure our ML solutions deliver value and meet business needs.
About the Role:
This is an exciting opportunity to join our team of experts in machine learning and artificial intelligence. As a Senior ML Engineer, you will have the chance to design, develop, and deploy state-of-the-art machine learning models and algorithms that address complex business problems and enhance our products and services.
Key Responsibilities:
- Design, implement, and deploy machine learning models and solutions that address business problems and enhance products and services.
- Collaborate with data scientists and engineers to develop scalable data pipelines, optimize algorithms, and manage data workflows.
- Analyze large, complex datasets and build predictive models and systems that improve business processes and decision-making.
- Research and apply advanced machine learning techniques, such as deep learning, reinforcement learning, natural language processing, and computer vision, depending on the project needs.
- Optimize the performance of ML models, ensuring they are efficient and meet performance requirements in terms of speed and accuracy.
- Integrate ML models into production environments and ensure that they work seamlessly with other parts of the tech stack.
Requirements:
To be successful in this role, you should have:
- A Bachelor's, Master's, or Ph.D. degree in Computer Science, Data Science, Engineering, or a related field.
- 8+ years of experience in machine learning engineering or data science with a focus on building, deploying, and maintaining machine learning systems.
- Strong proficiency in programming languages such as Python, R, or Java.
- Extensive experience with ML frameworks such as Scikit-learn, TensorFlow, or similar.
- Experience with NLP, RAGs, Agentic frameworks, or other advanced ML techniques.
- Strong understanding of data structures, algorithms, and software engineering principles.
- Hands-on experience with cloud platforms such as AWS, Google Cloud, or Azure for deploying ML models and managing infrastructure.
- Proficiency in SQL and working with large datasets (e.g., in relational databases, NoSQL systems).
- Knowledge of model deployment, monitoring, and optimization in a production environment.
PREFERRED QUALIFICATIONS:
- Experience with Snowflake
- Familiarity with MLOps tools and practices for automating and managing ML workflows.
- Knowledge of data privacy regulations (e.g., GDPR) and best practices for handling sensitive data.
- Contributions to the open-source community or publications in relevant journals.
