Staff Engineer I

What you'll do

• Lead the experimentation, design and build of agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling.
• Design intelligent agents that can automate repetitive decision-making tasks such as candidate generation tuning, feature selection, or context-aware content curation.
• Support POCs/Pilots projects to explore and validate innovative technologies and solutions in ML/AI space.
• Build and operate machine learning models on diverse, high-volume data sources for forecasting, classification and prediction.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Design pipelines for data preparation, model training, evaluation, and deployment in collaboration with engineering counterparts.
• Define best practices for monitoring ML model performance using statistical techniques; identifying drifts, failure modes, and improvement opportunities.
• Contribute to ML/AI thought leadership through blogs, case studies, internal tech talks, and industry conferences.
• Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
• Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.
• Document designs, development processes, and best practices to promote knowledge sharing and operational efficiency.

Requirements

• Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
• 8+ years of industry experience in ML/Data Science, with deep proficiency in Python and one or more frameworks: PyTorch, TensorFlow, Scikit-learn.
• Solid understanding of ML fundamentals, regression, tree-based models, clustering, and time series.
• Hands-on experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
• Experience building Agentic frameworks such as LangGraph, AutoGen, CrewAI, n8n or ReACT-style agents.
• Expertise with algorithms in NLP, Time Series, and Deep Learning, applied on real-world datasets.
• Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
• Experience with Model Context Protocols (MCPs) — building or integrating MCP tools, servers, or capabilities.
• Comfortable working in cross-functional teams.
• Strong problem-solving and analytical skills with the ability to work in agile environments.
• Good understanding of data pipelines, APIs, and cloud platforms (Azure/GCP/AWS).
• Excellent communication skills with the ability to simplify complex technical concepts.

Tech stack

Benefits