Agentic AI specialists

Company Description

Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 28,200+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.

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,



 

Qualifications

Educational qualification:

BE,BTECH or PHD

Experience :

7-11 years

Mandatory/requires Skills : AI

Preferred Skills :