Gen AI Architect

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