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 (GitHub Actions, Jenkins) Model monitoring (concept drift, latency, cost optimization) Experience with cloud platforms: AWS/GCP/Azure with AI services (SageMaker, Vertex AI, Bedrocknice 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 PoC building, quick prototyping, and converting them into production systems. Capability to estimate feasibility, complexity, cost, and timelines of ML solutions.