Staff Machine Learning Engineer

Automation Anywhere is a leader in AI-powered process automation that puts AI to work across organizations. The company’s Automation Success Platform is powered with specialized AI, generative AI and offers process discovery, RPA, end-to-end process orchestration, document processing, and analytics, with a security and governance-first approach. Automation Anywhere empowers organizations worldwide to unleash productivity gains, drive innovation, improve customer service and accelerate business growth. The company is guided by its vision to fuel the future of work by unleashing human potential through AI-powered automation. Learn more at

Key Responsibilities:

• Bachelor’s or Master’s Degree in Computer Science, Data Science, or related fields. Advanced degrees are a plus.
• 6+ years of hands-on experience in building and deploying machine learning models, with a focus on NLP, Computer Vision, or GenAI solutions.
• Proven experience deploying machine learning models into production environments, ensuring high availability, scalability, and reliability.
• Proficiency with modern ML frameworks (e.g., TensorFlow, PyTorch).
• Experience in building ML pipelines and implementing MLOps for automating and scaling machine learning workflows.
• Strong programming skills in Python, R, SQL, and experience with big data technologies (e.g., Spark, Hadoop) for data processing and analytics.
• Basic proficiency in at least one cloud-based ML services (e.g., AWS SageMaker, Azure ML, Google AI Platform) for training, deploying, and scaling machine learning models.
• Hands-on experience with containerization (Docker), orchestration (Kubernetes), and model serving platforms (e.g., Triton Inference Server, ONNX) for production-ready ML deployments.
• Familiarity with end-to-end ML pipelines, including data collection, feature engineering, model training, and model evaluation.
• Knowledge of model optimization techniques (e.g., quantization, pruning) to improve inference performance on cloud or edge devices.
• Excellent problem-solving skills, with the ability to break down complex challenges in document extraction and transform them into scalable ML solutions.
• Strong communication skills, with the ability to articulate ML problems clearly and work autonomously.