Agentic AI specialists

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 systems 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 DALLE 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 :

BEBTECH or PHD

Additional Information :

7-11 years

Remote Work :

No

Employment Type :

Full-time