Back
Mid-level ML title, metro location, and popular domain amplify candidate competition significantly.
Specialized agentic/LLM production expertise and eval/architecture ownership limit easy cross-industry transferability.
Explicit years, mandatory production agentic experience, and specific ML infra and evaluation requirements make filters highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Set and defend the technical architecture and design decisions for agentic AI systems on the Aera Platform, including build-vs-buy and model selection.
End-to-end ownership of the agentic platform: multi-step reasoning, tool use, subagent orchestration, agent memory, autonomous loops with human checkpoints, and agent security design.
Own organizational evaluation discipline for AI features, implementing offline/online evaluations, golden datasets, LLM judgments, regression gates, and reusable platform components to boost reliability and performance.
B.E./B.Tech in Computer Science, Computer Engineering, or related field.
5–8 years software engineering and architecture experience; at least 3 years designing/deploying ML or LLM-based systems; minimum 12 months experience building agentic or LLM-powered systems in production.
Proven experience setting technical direction with ownership of design docs, architectural decisions, and production system lifecycle including incident handling.
Strong skills in Python, FastAPI, distributed systems, ML frameworks (PyTorch, Hugging Face, scikit-learn, pandas), containerized microservices (Docker, Kubernetes), and CI/CD tools.
Experienced systems thinker who abstracts complex business problems into reusable platform building blocks across domains.
Has taken full ownership of agentic AI systems from architecture through production reliability, with deep insight into failure modes and evaluation.
An advanced user of agentic coding tools with rigor in engineering discipline, evaluation, and security considerations, and able to clearly communicate and advocate technical decisions.