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Popular ML title, mid-level experience band, and broad full-stack/GenAI requirements increase candidate competition.
Core ML/NLP skills are transferable across industries, but agentic GenAI and Azure tooling create moderate specialization.
Explicit 2+ years plus many mandatory ML, GenAI, full-stack, and Azure skills increases filtering strictness.
Design, develop, and deploy advanced machine learning and NLP models, including agentic and generative AI applications with Retrieval-Augmented Generation (RAG) systems.
Build and maintain full-stack applications (front-end and back-end) integrating AI/ML and GenAI capabilities for internal and external users.
Collaborate cross-functionally to translate business needs into AI-driven solutions and ensure data-driven decision-making.
Bachelor's degree or higher in Computer Science, AI/ML, or related field.
Minimum 2+ years of professional experience in Python, Machine Learning, and Deep Learning.
Proficiency in Python frameworks for NLP (e.g., NLTK, Transformers) and machine learning libraries (TensorFlow, PyTorch, scikit-learn).
Experience in full-stack development including front-end (JavaScript/TypeScript, React) and back-end (FastAPI, Flask, Django) with AI/ML integration.
Experienced in designing and deploying agentic AI/GenAI solutions using RAG architectures, LLM orchestration frameworks (LangChain, LlamaIndex, etc.), and vector databases (FAISS, Pinecone).
Comfortable managing complete AI/ML software lifecycle including MLOps and scalable AI/ML architecture design in cloud environments (preferably Azure).
Skilled at handling complex data pre-processing, feature engineering, and statistical data analysis to drive business impact.