Machine Learning (ML) Engineer

About the job
CloudAEye is recognized as "Top 10 Startups in AI" by AngelList. TechCrunch named us one of the Top AI Startup Battlefield companies. StartupGrind called us "an exciting AI startup that’s pioneering the integration of artificial intelligence into cloud operations".

CloudAEye’s mission is to enable customers achieve their business objectives by providing them with AIOps solution. An AIOps platform combines big data and machine learning functionality to support primary IT operations functions through the scalable ingestion and analysis of the ever-increasing volume, variety and velocity of data generated by IT.

We are looking for a senior engineer to develop AI-powered Cloud services and infrastructure components for our SaaS (Software as a Service). You will be responsible for delivering AI components that powers our SaaS services (ex. Root Cause Analysis, Test Failure Analysis, Code Review) running on the public cloud (ex. AWS).

We encourage all engineers to take ownership over technical and product decisions, closely interact with customers to collect feedback, and contribute to a thoughtful, fair and inclusive team culture.

At CloudAEye, you will

• Use latest generative AI techniques such as Agentic AI. Develop sophisticated agents using LLM.
  
  • Build intelligent agents capable of handling diverse tasks, autonomously plan and iteratively solve tasks based on a goal.
  • Create conversational chatbot using the latest generative AI techniques
• Develop machine learning and deep learning models using state of the art frameworks such as TensorFlow, PyTorch. Examples of work include:
  • Build models to predict future values of a time-series using forecasting models
  • Use advanced Natural Language Processing (NLP) models
    
  • Use reinforcement learning, knowledge graph, multivariate statistics, etc.
  • Use deep generative models such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE)
  • Perform hyperparameter tuning using Bayesian Optimization
• Works closely with Cloud Engineering team, and Product Management to deliver industry's leading Cloud solution
• Develop system architecture, design and build platform services for scalable, resilient, multi-tenant Cloud SaaS
• Work with customers, partners, and business leaders to architect solutions for Cloud Operations

We’re looking for someone who

• Has 3+ years of experience with machine learning (ML)
• Has strong experience with Python, NodeJS
• Has MS or Ph.D. in Computer Science or associated fields with extensive background in AI/ML
• Has basic knowledge of security
• Has knowledge of good engineering practices and agile culture
• Takes initiative in a fast-paced environment

Nice to have

• Familiar with LLMs - OpenAI, Anthropic, Llama, etc.
  
• Familiar with MLOps / productization of ML/DL models at scale
• Familiar with public clouds (AWS, GCP, Azure)
• Familiar with containerization or cluster orchestration such as Kubernetes, Docker
• Familiar with MLflow, Bedrock, AWS SageMaker
• Experience working remotely in a distributed team
• Startup experience

Location
Remote - India, Bangladesh, Brazil, China, Czech Republic, Poland, Austria

About us
We are a startup based in Bay Area, California. This is a great opportunity to join an AI/ML startup at the ground floor level. Our founding team consists of members with executive MBA from the University of Michigan Ross School of Business. We have assembled core team with backgrounds in AI/ML, Cloud, DevOps, B2B, SaaS, startups, and executive leadership in Fortune 500 companies.

CloudAEye is an equal opportunity employer. Our team comes from diverse background and we actively embrace and celebrate diversity and inclusion in our culture. We seek team members with moral courage and instincts to lead. We encourage all members to take ownership over decisions, be bold, focus on impact and contribute to a thoughtful, dynamic team culture.

We offer flexible working environment, an opportunity to work in a close knit group, work in a high performance team culture, opportunity to learn from talented individuals and domain experts from the industry, and an equity and compensation package that is competitive.