Key skills
AirflowAWSDockerNumpyPandasPythonPyTorchScikit-LearnAIMachine LearningMLLLMLangChainAgenticscikit-learnNumPyMLflowLangGraphCommunication
About this role
Role Overview
- Design, implement, and optimize agentic AI components, including context engineering, memory management, and prompts.
- Develop and maintain golden datasets by defining sourcing strategies, working with data vendors, and ensuring quality and representativeness at scale.
- Prototype and evaluate novel prompting strategies and reasoning chains for model reliability and interpretability.
- 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 agents through rigorous testing and continuous improvement.
- Maintain a strong bias for action—delivering incremental, well-tested improvements that directly enhance customer experience.
Requirements
- B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field.
- 4-6 years of hands-on industry experience with demonstrable ownership and delivery.
- Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.
- Proficiency in Python and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.
- Working knowledge of LLM core concepts, prompt design, and agentic design patterns.
- Prior experience building and deploying AI agents or LLM applications in production.
- Familiarity with modern agentic AI frameworks (e.g., LangGraph, LangChain, CrewAI).
- Experience with ML infrastructure and tooling (PyTorch, MLflow, Airflow, Docker, AWS).
- Exposure to LLM Ops
- infrastructure optimization, observability, latency, and cost monitoring.
- Strong communication skills and a passion for shaping emerging AI paradigms.
Tech Stack
- Airflow
- AWS
- Docker
- Numpy
- Pandas
- Python
- PyTorch
- Scikit-Learn
Benefits
- Compensation may vary based on a variety of factors, which include (but aren't limited to) role level, skills and competencies, qualifications, knowledge, location, and experience. In addition to base pay, certain roles are eligible to participate in our bonus or commission plans, as well as our benefits offerings and equity awards.