Job Summary
Required SkillsML & Statistical FoundationsSolid grasp of core statistics and probability - distributions, hypothesis testing, confidence intervals, and significance testingStrong understanding of supervised, unsupervised, and semi-supervised learning paradigms and when to apply eachWorking knowledge of the bias-variance tradeoff, regularization, and overfitting/underfitting diagnosisComfort with model evaluation metrics (precision, recall, F1, ROC-AUC) and how threshold choices affect themPractical experience with classical ML algorithms (regression, classification, clustering, ensemble methods) as a foundation for designing and evaluating GenAI systemsUnderstanding of optimization fundamentals (gradient descent, learning rate tuning, convergence behavior) relevant to model fine-tuningGenAI & Engineering SkillsStrong Python coding skills - comfortable building experimental pipelines and tooling from scratchDeep understanding of LLMs, Transformer architecture, and GenAI fundamentalsHands-on experience with Agentic AI, RAG, and LLM fine-tuningProficiency in LangChain, LangGraph, and MCP (Model Context Protocol)Experience with vector databases for retrieval experimentation (FAISS, ChromaDB, Pinecone)Strong grasp of evaluation methodologies for GenAI systems (groundedness, hallucination detection, relevance scoring)
Key Responsibilities
Required SkillsML & Statistical FoundationsSolid grasp of core statistics and probability - distributions, hypothesis testing, confidence intervals, and significance testingStrong understanding of supervised, unsupervised, and semi-supervised learning paradigms and when to apply eachWorking knowledge of the bias-variance tradeoff, regularization, and overfitting/underfitting diagnosisComfort with model evaluation metrics (precision, recall, F1, ROC-AUC) and how threshold choices affect themPractical experience with classical ML algorithms (regression, classification, clustering, ensemble methods) as a foundation for designing and evaluating GenAI systemsUnderstanding of optimization fundamentals (gradient descent, learning rate tuning, convergence behavior) relevant to model fine-tuningGenAI & Engineering SkillsStrong Python coding skills - comfortable building experimental pipelines and tooling from scratchDeep understanding of LLMs, Transformer architecture, and GenAI fundamentalsHands-on experience with Agentic AI, RAG, and LLM fine-tuningProficiency in LangChain, LangGraph, and MCP (Model Context Protocol)Experience with vector databases for retrieval experimentation (FAISS, ChromaDB, Pinecone)Strong grasp of evaluation methodologies for GenAI systems (groundedness, hallucination detection, relevance scoring)
Skill Requirements
Required SkillsML & Statistical FoundationsSolid grasp of core statistics and probability - distributions, hypothesis testing, confidence intervals, and significance testingStrong understanding of supervised, unsupervised, and semi-supervised learning paradigms and when to apply eachWorking knowledge of the bias-variance tradeoff, regularization, and overfitting/underfitting diagnosisComfort with model evaluation metrics (precision, recall, F1, ROC-AUC) and how threshold choices affect themPractical experience with classical ML algorithms (regression, classification, clustering, ensemble methods) as a foundation for designing and evaluating GenAI systemsUnderstanding of optimization fundamentals (gradient descent, learning rate tuning, convergence behavior) relevant to model fine-tuningGenAI & Engineering SkillsStrong Python coding skills - comfortable building experimental pipelines and tooling from scratchDeep understanding of LLMs, Transformer architecture, and GenAI fundamentalsHands-on experience with Agentic AI, RAG, and LLM fine-tuningProficiency in LangChain, LangGraph, and MCP (Model Context Protocol)Experience with vector databases for retrieval experimentation (FAISS, ChromaDB, Pinecone)Strong grasp of evaluation methodologies for GenAI systems (groundedness, hallucination detection, relevance scoring)