Principal Machine Learning Engineer

at  SPLORE PTE LTD

WHAT IS SPLORE?

Founded and funded by Temasek and Menyala, Splore is a Human Centric AI Platform that elevates and enhances the customer and employee experience, with a focus on revenue acceleration and cost efficiency.
Splore’s proprietary OS is powered by a trust-and-accuracy-focused, continually learning, multi-agent Answer Engine that can respond to complex queries and execute tasks with support for human-in-the-loop.
Splore empowers businesses to unlock the vast potential of not only their information assets but also leverage Splore’s proprietary knowledge index and language models, thereby enabling them to make quicker, more informed decisions. Users get fast, relevant, and trusted answers, thereby eliminating the all-too-common frustration of sifting through irrelevant information or waiting for answers.
Crafted by experts in machine learning, distributed systems, and enterprise applications, Splore blends data from the web with proprietary information and enterprise data to provide tailored results.
Our mission is to help elevate human experiences through empathic access to services and information.

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.

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.