Informacje o stanowisku

Our customer is a leading consulting, software, and technology company servicing industries such as healthcare, private equity, technology, and more. It develops products that create value and deliver company results across critical areas of its business, including portfolio strategy, customer insights, research and development, operational and technology transformation, marketing strategy, and many more. Besides, the company is at the forefront of innovation, actively expanding its AI services to deliver cutting-edge solutions.

Responsibilities:

• Develop, refine, and use ML engineering platforms and components.
• Ensure our programs can efficiently handle large volumes of data and meet deadlines.
• Establish and manage processes for models, including data preparation and prediction.
• Monitor model performance closely and address any issues promptly.
• Collaborate closely with client-facing teams to understand their needs and provide technical support.
• Translate client requirements into straightforward features.
• Write robust code that is easy to test, maintain, and troubleshoot.
• Maintain high standards by adhering to guidelines, participating in code reviews, and ensuring code quality.
• Thoroughly test all components to anticipate and resolve potential issues.
• Utilize tools for issue tracking, code review, and version control.
• Actively participate in team meetings to discuss progress and future plans.
• Stay updated on the latest developments in technology and explore innovative solutions.

Requirements

Must have:

• A degree in Computer Science, Engineering, Mathematics, or a relevant field.
• At least 3 years of practical experience developing machine learning (ML) solutions.
• Minimum 2 years of experience in deploying and managing ML models.
• Proficient in crafting ML models for optimal performance and scalability.
• Skilled in creating feature engineering processes, inference pipelines, and real-time model predictions.
• Strong programming skills in Python, Scala, or Java.
• Experience with distributed computing frameworks like Spark (PySpark).
• Experience with ML platforms like SageMaker, Kubeflow, MLFlow or similar.
• Familiarity with ML Ops for assessing and monitoring model performance.
• Proficiency in deploying models on cloud platforms like AWS, Azure, or GCP.
• Solid understanding of machine learning and deep learning principles.
• Knowledge of fundamental computer science concepts, including common data structures and algorithms.
• Ability to collaborate effectively with diverse teams. Excellent English language skills and communication abilities.

Nice to have:

• Familiarity with DevOps concepts, CI/CD pipelines, and data security measures, along with expertise in cloud platform architecture.

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