Agentic AI specialists

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, Educational qualification: BE,BTECH or PHD Experience : 7-11 years

Created: 2025-07-20