Senior machine learning engineer

Ability to design end-to-end ML system architecture with: Model orchestration (LLM + OCR + embeddings + prompt pipelines) Preprocessing for images/PDF/PPT/Excel Embedding store, vector DB, or structured extraction systems Async processing queue, job orchestration, microservice design GPU/CPU deployment strategy Must be strong in scaling ML systems: Batch processing large files Handling concurrency, throughput, latency Model selection, distillation, quantization (GGUF, ONNX) CI/CD for ML (Git Hub Actions, Jenkins) Model monitoring (concept drift, latency, cost optimization) Experience with cloud platforms: AWS/GCP/Azure with AI services (Sage Maker, Vertex AI, Bedrock—nice to have) Problem-Solving & Solution Ownership Able to identify the right ML approach (fine-tuning, retrieval, prompting, multimodal pipeline). Ability to break vague product problems into clear ML tasks. Skilled in Po C building, quick prototyping, and converting them into production systems. Capability to estimate feasibility, complexity, cost, and timelines of ML solutions.