AI Summary
Design, build, and improve large-scale machine learning systems. Provide technical leadership in improving production machine learning systems. Work on production systems where scalability, accuracy, speed, reliability, and operational efficiency are critical.
Key Highlights
Design, build, and improve large-scale machine learning systems
Provide technical leadership in improving production machine learning systems
Work on production systems where scalability, accuracy, speed, reliability, and operational efficiency are critical
Key Responsibilities
Provide technical leadership in improving the scalability, stability, accuracy, speed, and efficiency of production machine learning systems
Design, build, operate, and scale machine learning processing pipelines
Develop production-grade systems using technologies such as Python, Scala, Spark, SQL, Hadoop, cloud object storage, and Linux-based environments
Work with machine learning models such as gradient boosting, random forests, regression models, and neural networks
Design, develop, test, and deploy reusable libraries, frameworks, services, and core machine learning infrastructure
Build systems that enable machine learning models to be deployed and operated reliably at scale
Work closely with product managers, machine learning practitioners, data teams, and software engineers to translate business problems into practical technical solutions
Automate large-scale data processing, model execution, analysis, and operational workflows
Maintain high standards of software design, testing, code quality, observability, and production reliability
Evaluate technical trade-offs across model accuracy, system performance, scalability, maintainability, and delivery speed
Support and mentor other engineers through technical guidance, design reviews, and engineering best practices
Take end-to-end ownership of systems throughout their production lifecycle
Technical Skills Required
Benefits & Perks
Relocation sponsorship is provided
Job Description
Who is our client?
Our client is a large-scale technology organization with a strong focus on engineering, data, and AI. They are continuing to invest in AI/ML platform capabilities that enable teams to build, operate, and scale machine learning and intelligent applications more effectively.
About the Role
We are hiring a Senior-Tech Lead Machine Learning Engineer to design, build, and improve large-scale machine learning systems supporting automated optimization and decision-making.
This is a hands-on technical leadership role for an experienced engineer who combines strong software engineering fundamentals with practical machine learning expertise. You will work on production systems where scalability, accuracy, speed, reliability, and operational efficiency are critical.
You will take end-to-end ownership of machine learning solutions, from system and pipeline design through deployment, monitoring, and continuous improvement.
- Relocation sponsorship is provided
- Provide technical leadership in improving the scalability, stability, accuracy, speed, and efficiency of production machine learning systems.
- Design, build, operate, and scale machine learning processing pipelines.
- Develop production-grade systems using technologies such as Python, Scala, Spark, SQL, Hadoop, cloud object storage, and Linux-based environments.
- Work with machine learning models such as gradient boosting, random forests, regression models, and neural networks.
- Design, develop, test, and deploy reusable libraries, frameworks, services, and core machine learning infrastructure.
- Build systems that enable machine learning models to be deployed and operated reliably at scale.
- Work closely with product managers, machine learning practitioners, data teams, and software engineers to translate business problems into practical technical solutions.
- Automate large-scale data processing, model execution, analysis, and operational workflows.
- Maintain high standards of software design, testing, code quality, observability, and production reliability.
- Evaluate technical trade-offs across model accuracy, system performance, scalability, maintainability, and delivery speed.
- Support and mentor other engineers through technical guidance, design reviews, and engineering best practices.
- Take end-to-end ownership of systems throughout their production lifecycle.
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- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field.
- At least 7 years of professional software engineering experience, including significant experience building machine learning systems and hands-on experience working in a production environment.
- Advanced proficiency in Python and SQL.
- Strong experience with at least one JVM-based programming language, preferably Java or Scala.
- Hands-on experience building or operating large-scale data and machine learning pipelines.
- Strong understanding of software architecture, system design, design patterns, object-oriented programming, and functional programming.
- Practical understanding of common machine learning algorithms and how they behave in production environments.
- Strong analytical and structured problem-solving capabilities.
- Demonstrated ability to design reliable, scalable, and maintainable production systems.
- Strong engineering mindset with a focus on ownership, code quality, testing, and operational excellence.
- Ability to communicate technical decisions clearly and work effectively with cross-functional stakeholders.
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