ML Engineer

Responsibilities:
Build, deploy, and maintain production AI systems.
Take ownership of end-to-end ML pipelines (data ingestion, training, validation, deployment, monitoring).
Optimize models for performance, scalability, and reliability.
Work closely with founders, engineering, and product teams to ship AI features.
Monitor live models, handle retraining, and improve model quality over time.
Improve our MLOps and deployment infrastructure.
Requirements:
3+ years of experience building and running production AI / ML systems.
Strong Computer Science background and understanding in Natural Language Processing / Computer Vision.
Solid hands-on experience with Python, PyTorch, SQL, graph databases, load balancing, etc.
Experience tracking experiments and models using, e. g., MLflow.
Strong understanding of AI foundations, LLMs, and model internals.
Practical experience deploying AI systems using Docker, Docker Compose, and Kubernetes.
Familiarity with model monitoring, versioning, and CI/CD for ML.
Deep understanding and practical experience of basic (Gen)AI approaches, e. g., RAG.
Practical experience with a multi-agent AI system is a plus.
3+ years of work experience with Machine Learning.
3+ years of work experience with Python (Programming Language).
1+ years of work experience with AI Agents.
Experience scaling ML systems in production.
Strong MLOps mindset.
Cloud infrastructure experience (AWS, GCP, Azure).
Experience in a startup or product-driven environment.
Experience owning AI features in end-to-end production-grade environments.
Experience with MCP / A2A protocol and agentic frameworks is a plus.
DevOps experience is a plus.
Experience with the ETL process is a plus.

Experience
5-9 yrs