Principal ML Engineer - Splore

What is Splore?

Splore is redefining how enterprises harness the power of generative AI and multi-agent systems. We work closely with established partners across industries like finance, legal, and tech, enabling them to solve real-world challenges and drive productivity. We integrate state-of-the-art AI technologies into existing business workflows, offering end-to-end solutions that enhance decision-making and streamline operations.Backed by industry leaders Temasek and Menyala, and powered by a team of AI and machine learning experts, Splore delivers AI applications to stay ahead in a rapidly evolving, data-driven landscape.

 

What is the role?

We are seeking a Principal Machine Learning Engineer to lead our ML/AI team. In this role, you will oversee all aspects of the ML lifecycle, from feature strategy to productization and operation. Acting as the primary ML expert for the CEO and Product Managers, you will guide the development of our core ML capabilities and enhance engineering processes to scale ML products across target businesses. Leading a team of 4-6 AI software engineers, you will foster a culture that balances AI innovation with ML productization. Additionally, you will be responsible for the development, mentorship, and career growth of your team members.

 

Responsibilities

In this role, you will:

• ML System Architecture and Design: Architect, design, and oversee the implementation of ML systems with a strong focus on data pipelines, training/inferencing MLOps, and service/model performance tracking.
• AI Research: Stay updated with the latest advancements in AI, particularly generative AI; identify suitable technologies and models for Splore use cases, apply rigorous experimentation practices, and drive continuous ML feature upgrades.
• ML Evaluation and Improvement: Establish a consistent approach for model evaluation with an emphasis on business impact, enforce appropriate model training and evaluation practices with relevant metrics to justify business value.
• POC Management and Facilitation: Collaborate closely with Product Management and Sales to design and develop POC AI solutions, demonstrating and justifying their impact.
• ML Operations and Scaling: Assess business needs to design and implement suitable ML training and inferencing pipelines and frameworks, promote best practices for ML engineering in training and model optimization, and design mechanisms for evaluating and tracking model performance.
• Technical Leadership and Oversight: Provide technical guidance to AI engineers, ensuring high-quality technical delivery and continuous growth. Offer career advice and growth opportunities for engineers at various seniority levels.

 

Attributes

We are looking for a Principal Machine Learning Engineer with the following:

• Dealing with Ambiguity - You thrive in navigating dynamic environments, making informed decisions amid evolving scenarios and comfortably embracing uncertainty.
• Collaborates - We're all about teamwork here. You will work closely with the Product Management and Engineering teams to align long-term AI capabilities and solutions with both business and technical requirements. Strong communication skills are essential for liaising with stakeholders across engineering, product, business, and client teams.
• Nimble Learning - We're looking for someone who thrives in a startup environment. You're not afraid to get your hands dirty and learn through experimentation when faced with fresh challenges. You're always on the pulse of the latest ML trends and immersing yourself in new technologies. 
• Functional/ Technical Skills - MS/PhD in Engineering/Mathematics, Proven experience as a Principal, Technical Lead, or Engineering Manager building end-to-end ML systems (minimum 15+ years’ experience, including 10 years in AI/ML and at least 5 years specializing in DL/NLP).
  
  • High proficiency in Python and extensive hands-on experience with DL frameworks such as TensorFlow and PyTorch.
  • Expertise in MLOps practices, including continuous training, model deployment, and monitoring.
  • Strong software engineering skills in complex, multi-language systems and experience with large-scale microservice architecture, focusing on ML microservice availability.
  • Over 5 years of experience working with cloud platforms such as Microsoft Azure, AWS, or GCP, with expertise in cloud computing and database systems and a strong focus on scalable data pipelines (e.g., Spark, serverless architectures).
  • Ability to translate business needs into technical requirements, with a strong understanding of software testing, benchmarking, and continuous integration.