Remote Software engineering Jobs • MLOps

This hands-on technical role spans Generative AI (GenAI), traditional Machine Learning (ML), and early-stage Data Science. You’ll play a critical part in delivering high-impact AI solutions, establishing best practices, and supporting the development of our AI capabilities across the business. You will work across the full AI lifecycle - from prototype to production - and contribute to platform development, MLOps/LLMOps infrastructure, and applied data science.

As a Software Engineer on the ML Platform team, you will lead the effort to build systems that enable AI R&D teams to deliver secure, highly scalable, distributed services. You will leverage technology such as Kubeflow, Kubernetes, Helm, Terraform, Python to build inference systems, infrastructure, and training pipelines. You will also provide guidance and mentorship to junior engineers.

As a Machine Learning Engineering Manager at Fieldguide, you will combine your expertise in ML and generative AI with strong people leadership to drive our most critical AI initiatives. You’ll directly manage a team of ~5 machine learning engineers working across multiple product teams, guiding them in delivering first-of-their-kind ML features for the audit and advisory industry.

Shape the future of AI within Apptegy's product suite by embedding cutting-edge LLM capabilities directly into user experiences. Solve complex problems at the intersection of product, engineering, and artificial intelligence. Mentor an engineering organization, establish AI best practices, and see your strategic vision come to life, growing your leadership and technical expertise.

As a Senior Machine Learning Engineer, engineer beautiful code in Python and take pride in what you produce, advocating for high-quality engineering and best-practice in production software as well as rapid prototypes. This hands-on technical role involves leading projects, client discussions, building trusted relationships, finding creative ways to use machine learning to solve problems, scoping projects, and overseeing the delivery of engagements.

As a Senior LLM & AI Engineer, you’ll lead the design, deployment, and optimization of AI-powered applications that incorporate large language models (LLMs). You’ll collaborate across technical and business teams to deliver innovative, scalable solutions while contributing to best practices in MLOps and AIOps. You’ll help steer responsible AI use and stay ahead of emerging trends to guide platform evolution.

You will mainly be responsible for developing a new product utilizing machine learning techniques and generative AI from a proof of concept into a releasable product. Responsibilities include creating effective prompts to maximize the quality of the output from LLMs, building machine learning models, working with engineers and product leads, and creating PoC applications to validate ideas.

As an integral part of the program, the Senior Machine Learning Engineer leads the team in evaluating new or emerging technologies using prototypes and/or proof of concepts, analyzes and communicates the benefits and risks in implementing solutions using the new technologies, and leads teams to support the adoption of new technologies across the enterprise. The Senior Machine Learning Engineer will also provide technical leadership by supervising and mentoring junior IT specialists.

Shape AI that powers the future of financial access across Southeast Asia. You’ll play a key role in building and scaling systems that use AI to solve meaningful problems, from fraud detection and risk modeling to personalized experiences. This role combines senior-level engineering depth with team leadership, reporting directly to the Head of AI. You’ll be both an individual contributor and a technical mentor.

As an AI Engineer, you will work closely with Senior AI Engineers and MLOps/Data Engineers to develop, deploy, and optimize AI models that power our next-generation security products. This is a hands-on engineering role, requiring expertise in machine learning model development, deployment, and optimization. You will contribute to multiple AI initiatives, ensuring that models are efficient, scalable, and production-ready.