Machine Learning Engineer

• Implement and deploy generative AI models (e.g., large language models, diffusion models) based on architectural guidance and research specifications.
• Build and optimize agentic AI systems - autonomous agents that perceive, reason, and act effectively in complex environments using frameworks like LangChain, CrewAI, and Google Agent Development Kit (ADK).
• Work with Model Context Protocol (MCP) to enable seamless integration between AI agents and external tools and data sources.
• Develop production-ready implementations by translating insights from recent ML/AI research papers into functional code.
• Apply core NLP concepts including tokenization, embeddings, attention mechanisms, and transformer architectures in practical applications.
• Collaborate with senior engineers and data scientists to build scalable ML pipelines and deploy models into production environments.
• Develop and integrate APIs using FastAPI for model serving and application development.
• Implement prompt engineering techniques to optimize model performance and output quality.
• Conduct model evaluation and experimentation to assess performance, accuracy, and efficiency of ML systems.
• Maintain comprehensive technical documentation for implementations, experiments, and deployment processes.
• Learn from existing codebases and contribute to improving ML engineering best practices within the team.
• Take ownership of assigned tasks and ensure timely delivery of high-quality implementations.
• Support research efforts by implementing proof-of-concepts and contributing to internal knowledge sharing.
• Stay updated with the latest developments in generative AI, agents, and ML technologies through continuous learning.

Machine Learning Engineer

AI Engineer

Lead AI Developer

AI Developer, Technology Engineer

Senior Machine Learning Engineer