Sr. Data Scientist Engineer

Automation Anywhere

1d•Remote

About The Position

This role designs, develops, and deploys scalable AI and data-driven solutions leveraging machine learning, deep learning, natural language processing (NLP), and large language models (LLMs). The position is responsible for building intelligent agent architectures, prompt-driven workflows, and retrieval-augmented generation (RAG) pipelines that integrate enterprise data sources and vector databases to deliver impactful business outcomes. Working cross-functionally with Engineering, Product Management, and customer-facing teams, this individual translates business and customer requirements into production-ready AI solutions. Key responsibilities include designing and maintaining high-quality data pipelines, performing data preprocessing and model experimentation, and deploying AI/ML services through APIs, microservices, and cloud-native platforms. The role also focuses on system reliability, model evaluation, and continuous improvement. This includes developing quantitative and qualitative frameworks to assess model performance and LLM outputs, monitoring scalability and robustness of AI systems, proactively addressing technical risks and customer challenges, and contributing well-documented, modular, and maintainable AI/ML and data engineering solutions. Success in this role requires a commitment to operational excellence, innovation, and the ongoing advancement of AI capabilities across the organization.

Requirements

Master’s degree in Artificial Intelligence, Computer Science, Data Science, or related field
Minimum 3-years experience in relevant fields
Strong programming skills in Python, Java, SQL and data processing
Experience working with structured and unstructured data
Knowledge of machine learning, deep learning, NLP, and LLM systems
Deep knowledge of MCP and A2A protocols
Experience with model training, evaluation, and experimentation
Familiarity with prompt engineering, agent-based architectures, and retrieval systems
Experience with APIs, distributed systems, and cloud platforms
Ability to apply data science and AI techniques to real-world business problems

Responsibilities

Build, evaluate, and deploy machine learning, deep learning, NLP, and LLM-based applications
Develop LLM-powered agents and agentic AI systems
Design prompt engineering strategies and LLM evaluation pipelines
Implement retrieval-augmented generation (RAG) systems using vector databases and knowledge sources
Design and maintain scalable, reliable data pipelines and datasets
Perform data preprocessing, feature engineering, and model experimentation
Deploy AI/ML services via APIs, microservices, and cloud infrastructure
Monitor model performance and continuously improve system reliability and scalability
Collaborate cross-functionally to translate customer needs into technical solutions
Identify risks, manage customer pain points, and drive continuous improvement initiatives