The partner company is looking to build advanced AI systems that process and reason over complex, high-volume log data, requiring a Machine Learning Engineer to design and implement intelligent agentic components, optimize data pipelines, and develop innovative approaches to AI observability and model interpretability.
Requirements
- Proficiency in Python and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.
- Working knowledge of LLM core concepts, agentic AI design patterns, and prompt engineering.
- Experience deploying AI agents or LLM applications in production.
- Familiarity with agentic AI frameworks (e.g., LangChain, LangGraph, CrewAI).
- Experience with ML infrastructure and tooling (PyTorch, MLflow, Airflow, Docker, AWS).
- Knowledge of LLM Ops, including infrastructure optimization, observability, latency, and cost monitoring.
- Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.
Responsibilities
- Design, implement, and optimize agentic AI components, including memory management, context engineering, and prompt strategies.
- Develop and maintain high-quality datasets, ensuring representativeness and reliability for model training and evaluation.
- Prototype and evaluate novel prompting strategies, reasoning chains, and model interpretability techniques.
- Collaborate cross-functionally with product, data, and infrastructure teams to deliver end-to-end AI-powered insights.
- Operate autonomously in a fast-paced, ambiguous environment, defining scope, setting milestones, and driving outcomes.
- Ensure reliability, performance, and observability of deployed AI agents through rigorous testing and continuous improvement.
- Deliver incremental improvements that directly enhance the quality and utility of AI-powered systems.
Other
- 1–2 years of hands-on industry experience with demonstrable ownership and delivery.
- Strong collaboration and communication skills with a passion for shaping emerging AI paradigms.
- Ability to work effectively in an autonomous, fast-paced, and experimental environment.
- Located in the Pacific Time zone preferred.
