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Popular early-mid AI/ML title with broad LLM and CV requirements increases applicant competition moderately.
Specialized LLM, RAG and computer-vision tooling transfers across industries but requires domain-specific experience.
Explicit 2–3 year requirement plus many mandatory ML, CV, and cloud tools makes shortlisting strict.
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Develop and implement autonomous AI agent architectures with integrated advanced computer vision capabilities.
Design and optimize multi-modal AI workflows using Retrieval-Augmented Generation and orchestration frameworks like LangChain or LangGraph.
Manage end-to-end AI solution lifecycle including cloud deployment, monitoring, and performance optimization on AWS, Azure, or GCP.
2–3 years professional experience in software development focused on AI/ML lifecycle management.
Proficiency in Python and strong knowledge of LLMs, RAG, and Computer Vision architectures.
Experience with orchestration tools (LangChain or LangGraph) and vision libraries (e.g., OpenCV, PyTorch, YOLO, Detectron2, SAM, or ViT).
Familiarity with vector databases (Chroma, Pinecone, or FAISS) and cloud platforms (AWS, Azure, or GCP).
Experienced in transitioning AI models from development to production-grade, scalable API deployments.
Skilled in model optimization for low-latency inference using tools like ONNX or TensorRT and containerization technologies (Docker/Kubernetes).
Demonstrated ability to build complex, multi-modal AI systems combining generative AI, computer vision, and knowledge retrieval with practical delivery experience.