Staff Machine Learning Engineer

About the role

Join Automation Anywhere, the leader in Agentic Process Automation (APA), as a Staff Machine Learning Engineer. In this highly visible technical leadership role, you will design, build, and deploy cutting-edge machine learning systems that operate at real-world scale. You will work closely with product, engineering, data science, and platform teams to translate breakthrough research into high-impact production systems used by global enterprises. This is an opportunity to shape the future of intelligent automation at scale. Key missions: Designing, building, and deploying cutting-edge machine learning systems that operate at real-world scale, with a focus on Generative AI, Natural Language Processing, and Computer Vision.. Architecting robust ML infrastructure, championing modern MLOps practices, and optimizing performance, scalability, and reliability across distributed environments.. Leading efforts in data acquisition and preprocessing, including annotation and refinement of datasets to improve model accuracy, and staying updated with state-of-the-art ML research. Profile: - Hands-on experience implementing MLOps best practices, including CI/CD for ML, automated model versioning, monitoring for drift/performance, and workflow automation - Experience building and managing end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and lifecycle management - 7+ years of hands-on experience designing, building, and deploying machine learning models, with expertise in NLP, Computer Vision, and/or Generative AI solutions - Strong proficiency in Python (required) and working knowledge of R and SQL, with experience leveraging big data technologies (e.g., Spark, Hadoop) for large-scale data processing and analytics - Experience fine-tuning large language models (LLMs) and applying Generative AI techniques preferred - Practical experience with containerization and orchestration tools (e.g., Docker, Kubernetes) and model serving platforms (e.g., Triton, ONNX) for production-grade deployments - Experience with cloud-based ML platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform) for training, deploying, and scaling models in cloud environments - Proven experience taking ML models from development to production, ensuring scalability, reliability, high availability, and ongoing performance monitoring - Deep experience with modern ML frameworks such as TensorFlow and PyTorch, including model training, evaluation, optimization (e.g., quantization, pruning), and inference performance tuning - Familiarity with distributed training across multi-GPU or cloud environments preferred - Demonstrates curiosity and agility in staying ahead of rapidly evolving AI/ML advancements, quickly evaluating new technologies, and applying them responsibly to real-world enterprise challenges - Ability to work cross-functionally with engineering, product, and data teams, influence technical direction without formal authority, and drive alignment across stakeholders in a fast-paced environment - Capacity to connect technical ML solutions to broader business objectives, prioritize high-impact initiatives, and make pragmatic trade-offs that balance innovation with production reliability - Strong communication skills, with the ability to articulate ML problems clearly and work autonomously - Excellent problem-solving skills, with the ability to break down complex challenges in document extraction and transform them into scalable ML solutions