Machine Learning​/AI Engineer

Responsibilities

• Build and operate end-to-end ML and AI systems covering data preparation, model training, evaluation, inference, deployment, and production iteration.
• Develop production-grade AI infrastructure and backend services, including inference pipelines, orchestration layers, model-serving services, APIs, caching, batching, and streaming.
• Design and implement agentic AI workflows supporting multi-step planning, tool use, memory, failure handling, recovery, and integration with LLMs and external systems.
• Monitor, debug, and optimize AI systems in production using logging, metrics, tracing, alerting, and production signals to improve latency, throughput, cost, reliability, and safety.
• Develop and integrate AI-powered product features across frontend, backend, ML, and AI systems, using production performance and usage data to drive continuous improvements.

• Strong production software engineering fundamentals, with experience building and operating high-throughput, low-latency backend services.
• Hands‑on experience developing and deploying ML or AI systems, including model training, evaluation, inference, and production monitoring; experience with PyTorch .
• Experience with LLM-based systems and AI inference patterns, including OpenAI, Anthropic, or open-source LLMs , embeddings, tool calling, agent workflows, memory, or multimodal AI.
• Proficiency in Python and Node.js , with experience using SQL and/or No
  
  SQL databases , Docker , and Kubernetes .
• Experience debugging and optimizing distributed AI systems in production, including inference latency, throughput, caching, batching, streaming, observability, reliability, fallback mechanisms, and resource/cost optimization.