Job
Description
Roles & Responsibilities : Roles & Responsibilities : Education and Work Experience Requirements: 5 to 8 years of experience as Data Scientist 2 to 3 years of experience in Generative AI solution development 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. 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. Expertise in hierarchical agent frameworks, distributed agent coordination, and decentralized AI governance. Strong grasp of memory architectures, tool use, and action planning within AI agents. Autonomy Score: Measures the degree of independence in decision-making. Collaboration Efficiency: Evaluates the ability of agents to work together and share information. Task Completion Rate: Tracks the percentage of tasks successfully executed by agents. Response Time: Measures the latency in agent decision-making and execution. Adaptability Index: Assesses how well agents adjust to dynamic changes in the environment. Resource Utilization Efficiency: Evaluates computational and memory usage for optimization. Explainability & Interpretability Score: Ensures transparency in agent reasoning and outputs. Error Rate & Recovery Time: Tracks failures and the system s ability to self-correct. Knowledge Retention & Utilization: Measures how effectively agents recall and apply information. Hands-on experience with LLMs such as GPT, BERT, LLaMA, Mistral, Claude, Gemini, etc. Proven expertise in both open-source (LLaMA, Gemma, Mixtral) and closed-source (OpenAI GPT, Azure OpenAI, Claude, Gemini) LLMs. Advanced skills in prompt engineering, tuning, retrieval-augmented generation (RAG), reinforcement learning (RAFT), and LLM fine-tuning (PEFT, LoRA, QLoRA). Strong understanding of small language models (SLMs) like Phi-3 and BERT, along with Transformer architectures. Experience working with text-to-image models such as Stable Diffusion, DALL E, and Midjourney. Proficiency in vector databases such as Pinecone, Qdrant for knowledge retrieval in agentic AI systems. 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. Expertise in Python, R, TensorFlow, Keras, and PyTorch. Hands-on experience with NLP tools and libraries: OpenNLP, CoreNLP, WordNet, NLTK, SpaCy, Gensim, Knowledge Graphs, and LLM-based applications. Proficiency in advanced statistical methods and transformer-based text processing. Experience in reinforcement learning and planning techniques for autonomous agent behavior. 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. Experience in cloud services like Azure, GCP and AWS Multi-agent frameworks: AutoGen, LangGraph, LangChain, CrewAI Large Language Models (LLMs): GPT,
