Agentic AI specialists
7 hours ago
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, Qualifications BE,B Tech, MTech
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