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Specialized mid-level ML role with many mandatory tools; moderate candidate density.
Combines LLM and CV production skills, making background moderately transferable across industries.
Explicit 3–5 years plus numerous mandatory frameworks, cloud and MLOps requirements imply high shortlisting strictness.
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Own end-to-end development and deployment of production-grade Generative AI and Computer Vision models, including fine-tuning, inference pipeline design, and real-time optimization.
Address domain challenges such as LLM hallucination reduction, vector search scalability, real-time inference SLAs, and model concept drift detection and retraining.
Lead cross-functional collaboration to define performance SLAs, evangelize AI best practices, and mentor junior engineers on reproducible research and AI delivery.
3–5 years of professional experience deploying generative and vision-based AI models in production.
Proficiency in at least one tool from each required category: LLM frameworks (Hugging Face, Ollama, vLLM, LLaMA), agent/retrieval tools (LangChain, LangGraph, vector DBs), inference serving (Triton, FastAPI, Flask), CV frameworks (PyTorch, TensorFlow, OpenCV, DeepStream), model optimization (TensorRT, ONNX Runtime), MLOps (Docker, Kubernetes, MLflow, DVC), monitoring (Prometheus, Grafana), cloud platforms (AWS, GCP, or Azure).
Mandatory programming in Python (C++ or Go preferred).
Bachelor's or Master's degree in Computer Science, Electrical Engineering, AI/ML, or related field.
Experienced in solving complex AI production challenges involving multi-modal workflows integrating LLMs and vision models with scaled vector search and real-time inference.
Comfortable managing AI model lifecycle including deployment, monitoring, drift detection, and iterative improvement leveraging modern MLOps tools and cloud infrastructure.
Demonstrates technical leadership through mentoring, defining SLAs, and collaboration across product and DevOps teams in high-paced AI engineering environments.