Desired Competencies (Technical/Behavioral Competency) Must-Have Gen AI Experience in LLMs, Embedding Models, Prompt Engineering Knowledge of statistical programming languages like Python Good applied statistical skills Good knowledge of machine learning algorithms Proficiency in handling imperfections in data & generating insights and patterns. Experience with Data Visualization Tools Fine tuning of LLM Evaluation Framework (RAGAS, ROUGE, BLUE Scrore) Autoencoding model (Encoder: ROBERTA/ BERT/ DistilBERT) Langsmith framework Autoregressive model (Decoder: GPT/ BLOOM/ LLama/ Mistral/ Claude/ CodeGen/ OPT/ PaLM RAG/Multimodal Architecture using Langchain/ LlamIndex MCP Architecture Experience Agentic AI Framework ( Crew AI, AutoGen, LangGraph, Google AVD etc. ) Experience in Model deployment, LLMOps, Scalability and Performance Fine tuning of LLM AI ML exp. Candidate has exp. in Transformer Architecture (Encoder and decoders) Knowledge Graph implementation (If anyone has created knowledge graph, that's good) Good-to-Have Excellent Communication Skills Strong Software Engineering Background (Productionizing the models) Hands-on experience with data science tools Problem-solving aptitude Analytical mind and great business sense Responsibility of / Expectations from the Role Data mining or extracting usable data from valuable data sources. Using machine learning tools to select features, create and optimize predictive models. Carrying out preprocessing of structured and unstructured data Enhancing data collection procedures to include all relevant information for developing analytic systems. Processing, cleansing, and validating the integrity of data to be used for analysis. Analyzing large amounts of information to find patterns and solutions. Developing prediction systems and machine learning algorithms Ability to use LLMs to build GenAI powered solutions. Presenting results in a clear manner Propose solutions and strategies to tackle business challenges. Collaborate with Business and IT teams. Designing and developing machine learning and deep learning systems Knowledge of productionizing the ML and DL applications and monitoring