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ML Engineer

Remote:

Full Remote

Contract:

Full time

Salary:

6 - 75K yearly

Experience:

Mid-level (2-5 years)

Work from:

United Kingdom

Offer summary

Qualifications:

Master's degree in relevant field, Proven experience in ML models.

Key responsabilities:

Optimize and enhance ML models
Develop training pipelines
Deploy models using Docker, CI/CD
Collaborate with team members
Document processes and best practices

Kashmir Intelligence Information Technology & Services Startup https://kashmirintelligence.com/

2 - 10 Employees

See more Kashmir Intelligence offers

Job description

This is a remote position.

The mission

Kashmir Intelligence is a trailblazer in the energy industry. Our mission is to revolutionise
oil-and-gas. We are now looking to grow our team with talented, mission-driven people who are driven to improve themselves and deliver change in the world… Does that sound like you?

The opportunity

We are seeking a highly skilled Machine Learning Engineer to join our dynamic team. The ideal candidate will have a robust background in both machine learning research and software engineering, with a focus on optimising models for production. You will be responsible for enhancing model performance for real-time inference this includes quantising and/or conversion of models to TensorRT formats, improving training processes for speed, and managing end-to-end deployment using Docker and version control systems. Additionally, you will implement robust monitoring and health check systems to ensure our models remain reliable in production. Furthermore, you will be developing a self-learning pipeline that collects information for continuous learning of our models. If you are passionate about cutting-edge technology and thrive in a collaborative, fast-paced environment, we would love to hear from you.

This role offers more than a job; it's a calling for those passionate about making a significant impact in the energy sector through technological innovation. You'll not only be running a team of leading experts but also contributing to a project with monumental potential. If you're ready to pioneer AI solutions that will redefine industry standards and eager to grow alongside a company poised to become a market leader, Kashmir Intelligence is your next destination.

What will you be doing?

· Model Optimization and Real-time Inference:

o Enhance the performance and accuracy of machine learning models to meet the demands of real-time applications.

o Implement and optimize algorithms for efficient inference, minimising latency while maximising throughput.

o Use techniques such as quantization, pruning, and hardware-specific optimisations (e.g., for GPUs via TensorRT etc.) to improve model performance.

· Improved Training Processes:

o Develop and refine training pipelines to improve model convergence rates and overall performance.

o Implement distributed training solutions to handle large-scale data and models.

o Experiment with and apply advanced techniques such as transfer learning, hyper parameter tuning, and automated machine learning (AutoML).

· Deployment and Monitoring:

o Containerise machine learning models using Docker to ensure consistency and reliability across different environments.

o Develop and maintain CI/CD pipelines for seamless deployment of models to production environments.

o Implement version control systems for models and datasets to ensure reproducibility and traceability.

o Monitor model performance and health in production, implementing alerting and automated recovery mechanisms.

· Collaborative Development:

o Work closely with software engineers and ML researchers to integrate models into production systems.

o Collaborate with data scientists to understand model requirements and constraints, providing feedback to improve model design.

o Contribute to the development of shared tools and libraries to standardise and streamline machine learning workflows across the organisation.

· Documentation and Best Practices:

o Document processes, code, and methodologies to ensure knowledge transfer and maintainability.

o Establish and promote best practices for machine learning development, deployment, and monitoring.

Stay updated with the latest advancements in machine learning and related fields, applying new knowledge to improve our systems.

Requirements

What sort of experience, skills or attributes are we looking for?

To be effective in this role you’ll need to be / have:

● A master's degree in fields such as Computer Science, Computer Engineering, Machine Learning, Physics, or Mathematics, particularly with a focus on optimisation. The ideal candidate should have a notable history of achievements in their field.

● Proven experience in developing and deploying machine learning models in a production environment.

● Proficient in programming languages like Python and C++, with a minimum of 3 years of combined experience in academic and/or industry settings.

● Strong background in software engineering principles, including experience with version control (e.g., Git), containerisation (e.g., Docker), and CI/CD pipelines.

● Hands-on experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, etc.

● Familiarity with standard software development practices, including source control, bug tracking, and creating documentation.

● Experience with distributed computing frameworks (e.g., Apache Spark, Dask) and cloud platforms (e.g., AWS, GCP, Azure)

● Familiarity with orchestration tools like Kubernetes for managing containerized applications.