Gen AI

Education and Work Experience Requirements:

· 5 to 9 years of experience as GenAI or Agentic AI specialist

· 2 to 3 years of experience in Generative AI/ Agentic AI solution development

· Proven track record and experience with below technologies

o Open source LLMs like Llama, Gemma, Mixtral etc

o Closed source LLMs such as Open AI GPT, Azure Open AI, Claude, Gemini etc

o Prompt Engineering/Tuning, RAG, RAFT, LLM finetuning such as PEFT(LoRA, QLoRA ..)

o Understanding of SLMs such as Phi3, BERT and Transformer architecture

o Vector databases like Pincone, Qdrant etc.

· Good knowledge of advanced statistical methods. Experience working with Text Data using

transformer-based model

· Expertise with the following scripting languages:

o Python, R, Tensorflow, Keras, Pytorch

o OpenNLP, CoreNLP, WordNet, NLTK, SpaCy, Gensim, Large Language Models,

Knowledge Graphs

· Good and experience of machine learning algorithms and ability to apply them in supervised and

un-supervised NLP tasks.

· Strong understanding of AI agent collaboration, negotiation, and autonomous decision-making.

· Experience in developing and deploying AI agents that operate independently or collaboratively in

complex environments.

· Knowledge of NLP algorithms that can handle various NLP tasks such as intent recognition, entity

extraction, language modeling, text classification, question answering, text summarization, topic

modeling and so on

Experience building and fine-tuning Language Models (LMs), such as BERT, ELMo, XLNet etc to solve bespoke NLP tasks

. · Tech savy and willing to work with open-Source Tools

· Should have independently handled a project technically and provided directions to the other Team Members. 

Able to lead the project independently. 

· Experience in turning ideas into actionable designs. 

Able to persuade stakeholders and champion effective techniques through development. 

· Strong interpersonal and communication skills: ability to tell a clear, concise, actionable story with data, to folks across various levels of the company. 

· Good to have foundational knowledge on Cloud, API frameworks like Flask, Fast API, Swagger/Postman tools

Mandatory Skills:

· Design, develop, test, and deploy Machine Learning models using state-of-the-art algorithms with a

strong focus on language models.

· Strong understanding of LLMs, and associated technologies like RAG, Agents, VectorDB and

Guardrails

· Hand-on experience in GenAI frameworks like LlamaIndex, Langchain, Autogen, etc.

· Deep knowledge of agentic AI principles, including self-improving, self-organizing, and goal-driven

agents.

· Proficiency in multi-agent frameworks such as AutoGen, LangGraph, LangChain, and CrewAI for

orchestrating AI workflows.

· Hands-on experience integrating LLMs (GPT, LLaMA, Mistral, etc.) with agentic frameworks to

enhance automation and reasoning.

· NLP tools and libraries: OpenNLP, CoreNLP, WordNet, NLTK, SpaCy, Gensim

· Experience in cloud services.

· Interact with our research team and with key partners in the market to build end-to-end AI/ML/NLP

solutions: Conversational AI, document understanding and QnA.

· Mine and analyze data, applying statistical methods as necessary, pertaining to customers

discovery, and viewing experiences to identify critical product insights.

· Proactively develop new metrics and studies to quantify the value of different aspects of product.

· Drive efforts to enable product and engineering leaders to share your knowledge and insights

through clear and concise communication, education, and data visualization.

· Translate analytic insights into concrete, actionable recommendations for business or product

improvement.

· Build and improve reusable tools & modelling pipelines and support knowledge sharing across

several teams.

· Define and deploy best practices in Machine Learning & MLOps/LLMOps, mentor and teach

colleagues.

· Deep understanding of Human-Machine Interaction (HMI) frameworks within cloud and on-prem

environments.

· Strong grasp of deep learning architectures, including CNNs, RNNs, Transformers, GANs, and

VAEs.

· Partner closely with product and engineering leaders throughout the lifecycle of project

Additional Information:

· A Masters or PhD is preferred (Computer Science / Machine Learning/ Mechanical, etc) from tier 1

institutions. If Bachelors, Machine Learning or Computer Science specialization only