The Sr. AI Engineer will develop and deploy machine learning models, automate workflows, and collaborate with teams to optimize solutions in AI-powered automation.
About Us
Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation. Its APA system, built on the industry’s first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics—all delivered with enterprise-grade security and governance. Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential.
Key Activities:
•Build, train, and fine-tune machine learning models tailored to project requirements.
•Build, train, and fine-tune machine learning models tailored to project requirements.
•Clean, preprocess, and analyze datasets to ensure quality inputs for AI agent/RAG implementation or model training.
•Develop meaningful features to improve model performance and outcomes.
•Package and deploy trained models into production using tools like Docker, Kubernetes, or cloud services.
•Performance Monitoring: Track model performance in production environments and optimize for reliability and scalability.
•Pipeline Automation: Develop and maintain automated workflows for data ingestion, training, and deployment.
•Code Development: Write clean, maintainable, and efficient code following best practices.
•Experimentation: Test various ML algorithms and architectures to find the optimal solution for specific problems.
•Collaboration: Work closely with data scientists, product managers, and architects to align on technical objectives.
•Troubleshooting: Debug and resolve issues in data pipelines, model performance, and production systems.
•Documentation: Maintain comprehensive documentation for models, workflows, and codebases.
•Skill Enhancement: Continuously learn new techniques and tools, contributing to innovation in projects.
Skills & Qualifications:
Skills & Qualifications:
•3–6 years of hands-on experience in AI/ML development and deployment.
•Strong understanding of machine learning concepts, algorithms, and workflows, including supervised, unsupervised, and reinforcement learning.
•Proficiency in Python and experience with libraries like TensorFlow, PyTorch, Scikit-learn, or Hugging Face.
•Experience with MLOps practices, including model versioning, CI/CD pipelines, and monitoring tools (e.g., MLflow, Kubeflow, or SageMaker).
•Expertise in data preprocessing, feature engineering, and working with large-scale datasets using tools like Pandas, NumPy, Apache Spark, or Hadoop.
•Hands-on experience with AI/ML services on AWS, GCP, or Microsoft Azure. Have completed certifications from either of these hyper scaler providers.
•Strong background on cloud services from various cloud service providers that integrates with AI/ML solutions
•Strong programming skills in Python, Java, or other relevant languages;
•Knowledge of deploying ML models in production environments using Docker, Kubernetes, or cloud-native services.
•Understanding of scalable and efficient system architectures for AI/ML pipelines.
•Experience with Git and collaborative development workflows.
•Problem-solving skills with a focus on developing efficient and innovative solutions.
•Ability to explain technical details to peers and stakeholders clearly.
•Passion for staying updated on emerging trends in AI/ML technologies.
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.
Top Skills
Spark
AWS
Docker
GCP
Git
Hadoop
Hugging Face
Kubeflow
Kubernetes
Azure
Mlflow
Numpy
Pandas
Python
PyTorch
Sagemaker
Scikit-Learn
TensorFlow
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