Enterprise UI AI Machine Learning Engineer
Enterprise UI AI Machine Learning Engineer
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Job Description
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 industrys first Process Reasoning Engine (PRE) and specialized AI agents combines process discovery RPA end-to-end orchestration document processing and analyticsall 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.
Job Description
Key Responsibilities:
- Develop and optimize machine learning models leveraging NLP Computer Vision and GenAI.
- Architect and implement scalable ML pipelines for training validation deployment and monitoring of production models.
- Drive the development of large-scale ML infrastructure ensuring low-latency inference and efficient resource utilization across cloud and hybrid environments.
- Implement MLOps best practices automating model training validation deployment and performance monitoring.
- Work closely with data engineers software engineers and product teams to ensure seamless integration of ML solutions into production systems.
- Optimize ML models for performance scalability and efficiency leveraging techniques like quantization pruning and distributed training.
- Enhance model reliability by implementing automated monitoring CI/CD pipelines and versioning strategies.
- Lead efforts in data acquisition and preprocessing including annotation and refinement of datasets to improve model accuracy.
- Stay updated with state-of-the-art ML research identifying opportunities to integrate new techniques and technologies into production systems.
Educational Qualifications:
- Bachelors or Masters 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.
Nice to Have:
- Experience in fine-tuning large language models (LLMs) and applying GenAI techniques.
- 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 @ email address whether submitted by an individual or by an agency will not be eligible for an agency fee.
Employment Type
Full-Time
