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Mid-level AI/LLM role with broad required skillset and 3–6 years band increases applicant competition.
Core AI/ML and LLM skills are transferable across industries but enterprise production experience makes fit moderately specific.
Explicit 4–5 years plus mandatory AI/ML, LLM, MLOps, cloud, vector DB and deployment skills impose strict filters.
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Lead design, development, and deployment of AI/ML solutions across various business use cases including NLP, Computer Vision, and predictive analytics.
Build and optimize machine learning, deep learning models, and generative AI applications like RAG pipelines and AI agents for enterprise-grade systems.
Own model deployment, monitoring, performance optimization, and implement MLOps best practices including CI/CD and automated workflows.
4–5 years of hands-on experience in AI/ML software development and deploying enterprise AI solutions.
Strong expertise in Core AI/ML engineering with Python and frameworks like TensorFlow, PyTorch, Scikit-learn, Keras.
Experience with generative AI technologies including LLMs, prompt engineering, RAG architecture, and vector databases (FAISS, Pinecone, ChromaDB, or Weaviate).
Experience with backend API development (FastAPI, Flask) and familiarity with cloud platforms (AWS, Azure, or GCP).
Experienced in leading technical AI initiatives and mentoring engineers within enterprise production environments.
Strong technical skills spanning Core AI/ML, Generative AI, MLOps, and scalable system design.
Comfortable collaborating cross-functionally with Product, Engineering, Data, and Business teams and contributing to AI architecture discussions.