Key Responsibilities

Design, develop, and deploy production-grade machine learning and generative AI solutions
Build and improve Retrieval Augmented Generation (RAG) pipelines for knowledge search and document intelligence
Support context management and agent-based workflows for AI-driven applications
Architect and maintain MLOps pipelines for model training, deployment, and monitoring
Implement monitoring and feedback loops to improve model performance and reliability
Develop AI agents to enable workflow automation and decision support
Contribute to AI best practices and mentor junior team members

Day to Day Responsibilities

Implement context engineering and agent-to-agent communication patterns
Collaborate with data engineering teams on data pipelines and feature development
Partner with cross-functional stakeholders to deliver scalable AI solutions

Preferred Experience and Attributes

5+ years of experience in data science, machine learning, or applied AI
Experience working with generative AI and large language models in production or near-production environments
Strong foundation in traditional machine learning techniques
Experience with MLOps practices including deployment, versioning, and monitoring
Proficiency in Python and experience writing production-quality code
Experience with PyTorch and/or TensorFlow; Hugging Face experience is a plus
Exposure to cloud platforms such as AWS, Azure, or GCP
Strong communication skills and ability to work with both technical and non-technical stakeholders

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