AI Engineer

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

Key Activities

• 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 & Qualification Criteria

• 5–8 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.