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