- Core ML is a pivotal role designed for an engineer who excels at the intersection of traditional machine learning and the emerging frontier of autonomous agentic systems. You will be responsible for designing, developing, and deploying sophisticated AI solutions that range from classic computer vision models to complex Multi-Agent Systems that reason, plan, and execute tasks autonomously. This role requires a deep understanding of Large Language Model LLM orchestration alongside a rigorous foundation in statistical learning and MLOps.
Key Responsibilities Agentic Systems &
- Orchestration ● Design and implement Multi-Agent Systems MAS utilizing frameworks such as ADK, LangGraph, CrewAI, or AutoGen to solve complex, multi-step business logic. ● Develop advanced reasoning patterns including Chain-of-Thought, ReAct, and Reflexion to enhance agent reliability and decision-making.
● Architect Retrieval-Augmented Generation RAG pipelines with vector databases to provide agents with contextual enterprise knowledge.
Core Machine
Learning &
- Computer Vision ● Build and refine traditional ML models Gradient Boosted Trees, Random Forests, SVN for structured data analysis and predictive forecasting. ● Develop and deploy Computer Vision solutions, including object detection, image segmentation, and feature extraction using architectures like Transformers ViT and CNNs. ● Conduct rigorous model evaluation, hyperparameter tuning, and error analysis to ensure high precision and recall in production environments. Valuable-to-Have Skills MLOps &
- Infrastructure ● Establish and maintain end-to-end ML pipelines CI/CD/CT for automated training, testing, and deployment using tools like Vertex AI or Kubeflow. ● Implement robust monitoring and logging for agentic workflows to track "hallucination" rates, latency, and cost efficiency. ● Optimize model inference for scalability and performance, ensuring low-latency responses for real-time applications.