About Us
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 www.automationanywhere.com
Lead Engineer – Machine Learning
Job Description
We are seeking a highly skilled Lead Maching Learning Engineer with a strong background in Software Engineering and Machine Learning to join our team. In this role, you will work closely with product managers, data scientists, and engineering teams to develop state-of-the-art solutions for extracting and understanding information from diverse datasets including documents, webscreens, and automation workflows using advanced AI techniques with a focus on Generative AI (GenAI).
Key Responsibilities:
- Collaborate with cross-functional teams to define and develop machine learning models focused on computer vision, natural language processing (NLP), and generative AI (GenAI).
- Drive the development of large-scale production infrastructure for training and deploying ML models across multiple cloud providers and geographic regions, ensuring high availability and low-latency inference.
- Lead the design and development of ML pipelines that automate the extraction, parsing, and transformation of unstructured data, leveraging GenAI models for enhanced information retrieval and processing.
- Design, manage, and maintain ML infrastructure to ensure scalability, low-latency inference, and efficient resource utilization in production environments.
- Apply statistical models, ML algorithms, and data analytics to improve the accuracy and scalability of document extraction workflows.
- Drive the development of infrastructure for training and deploying large-scale ML models
- Work on industry leading MLOps best practices to automate model training, validation, deployment, and monitoring, ensuring continuous integration and delivery of ML models.
- Lead the efforts in data acquisition by working with customers to define requirements , creating annotated datasets, and refining data sources to improve model performance for ML solutions
- 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 document extraction, NLP, 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) and document processing tools (e.g., Tesseract, Textract).
- 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.
Nice to Have:
- Experience in fine-tuning large language models (LLMs) and applying GenAI techniques in document intelligence tasks.
- Experience with distributed training techniques to optimize large-scale model training across multiple GPUs or cloud environments.
- Familiarity with CI/CD pipelines for ML, automated model versioning, and monitoring tools for performance and drift in production models.
All unsolicited resumes submitted to any @automationanywhere.com email address, whether submitted by an individual or by an agency, will not be eligible for an agency fee.