Gen AI Architect

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

Work arrangement
No