Senior Software Engineer

6 days ago

Singapore OPTIMUM SOLUTIONS (SINGAPORE) PTE LTD Full time
Roles & Responsibilities

As a machine learning engineer in the ML Platform team, you will contribute to various aspects of our machine learning infrastructure, including LLM evaluation, compute layer setup, image generation, and large-scale inference. Your expertise will be instrumental in developing and optimizing our ML pipelines, ensuring scalability, and supporting the data science community.

This is a 12-months contract, we have multiple headcounts for LLM Engineers specializing in (image generation, LLMs, Ray)

Responsibilities:

  • Develop and Optimize LLM Evaluation Pipelines: Design and implement robust and scalable pipelines for evaluating the performance of large language models. Leverage your expertise in Ray to distribute evaluation tasks efficiently.
  • Implement Evaluation Metrics and Benchmarks: Develop and integrate a comprehensive suite of metrics and benchmarks to assess LLM performance across various tasks and domains.
  • Analyze and Interpret Evaluation Results: Perform in-depth analysis of evaluation results to identify strengths, weaknesses, and areas for improvement in our LLM models. Communicate findings clearly to stakeholders.
  • Deliver High-Quality AI Infrastructure Solutions: Design and develop the infrastructure to support Ray for distributed data processing and model training. Develop using GitOps to ensure the reproducibility of the system's cloud infrastructure on different Kubernetes clusters.
  • Develop Observability Solutions for Ray: Develop and integrate monitoring and alerting within the monitoring stack powered by Datadog, Prometheus, and Grafana. Contribute to the creation of runbooks and DevOps guides.
  • Support the Data Science Community: Work with the product team to socialize Ray's use and support users in running their jobs on the Ray clusters. Document and help users onboard to new platforms.
  • Develop an Image Generation Platform: Set up a configurable and scalable image generation platform. Ensure it has the functionality of tools like ComfyUI, but scalable to allow for parallel generation and fine-tuning of new models.
  • Build Customization for Inference: Design and develop the infrastructure for large-scale batch and real-time inference using state-of-the-art GenAI models. Integrate observability into system performance, queues, and overall cost into the platform. Build customization to support diverse models, fine-tuning, and different workload priorities.

Requirements:

  • Expertise in LLM Evaluation: Strong understanding of LLM evaluation methodologies, metrics, and benchmarks. Experience with evaluating LLMs for various tasks (e.g., text generation, question answering, summarization).
  • Proficiency in Distributed Computing: Experience with distributed computing frameworks like Ray or similar technologies for scaling LLM evaluation.
  • Solid Python and ML Skills: Strong programming skills in Python and experience with machine learning libraries and tools.
  • Kubernetes and Cloud Infrastructure: Experience with deploying and managing applications on Kubernetes. Familiarity with AWS.
  • In-Depth Knowledge of ML-OPS: Solid understanding of distributed computing for data processing. Knowledge of Ray is preferable, but other frameworks, such as Dask, Modin, Beam, Horovod, and Deepspeed, are also valued.
  • Good DevOps Background: Experience with Infrastructure as Code (IaC) such as code with Terraform preferred. Previous experience with GitOps solutions like ArgoCD is preferred. Good knowledge of Helm and Kustomize is also valued.
  • Understanding of Image Generation Models: Experience with image generation models such as stable diffusion and flux. Bonus if you know ComfyUI.
  • Understanding of ML Inference: Experience with ML inference using LLM and other generative models, especially in optimizing for performance.

Tell employers what skills you have

Recipes
TensorFlow
Machine Learning
Scalability
Kubernetes
Autonomy
Pipelines
Software Engineering
Formulation
Application Development
PyTorch
Python
Data Science
Scientific Computing

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