AI Lead

As a Gen AI Engineer with experience in AI/ML and at least 1 year in Gen AI or Agentic AI, you will play a critical role in designing, implementing, and scaling intelligent agents that autonomously plan, execute, and refine tasks across diverse enterprise use cases. Your hands-on experience with Gen AI, Agentic AI, Python, RAG Pipeline, and cloud platforms such as Azure or AWS will be key in shaping the future of autonomous AI solutions. **Key Responsibilities:** - Design and develop Agentic AI architectures using LLMs, planning modules, memory stores, and tool integration frameworks. - Implement task orchestration, decision reasoning, and self-reflection capabilities within autonomous agents. - Work with multi-agent workflows involving planning, tool use, dialogue, and adaptive learning loops. - Integrate and fine-tune LLMs (GPT, Llama, Claude, etc.) for use in agent workflows. - Collaborate with product and platform teams to deploy solutions on cloud environments (AWS, Azure, GCP). - Contribute to solution accelerators, reusable frameworks, and reference architectures for Gen AI use cases. - Optionally contribute to MLOps/LLMOps pipelines for continuous model integration, evaluation, and monitoring. **Required Skills & Experience:** - 4 years of experience in AI/ML/GenAI domains, with at least 1 year of experience in Generative AI. - Strong understanding of LLM architectures, Agentic AI, prompt engineering, RAG pipelines, and memory systems (e.g., vector DBs). - Experience in building agents using frameworks such as LangChain, AutoGen, CrewAI, Semantic Kernel, or similar. - Proficiency in Python and relevant Gen AI libraries (Transformers, HuggingFace, OpenAI API, etc.). - Experience deploying solutions on at least one cloud platform (AWS, Azure, or GCP). - Strong problem-solving skills and ability to work in a fast-paced, agile environment. - Strong Python programming skills. **Nice to Have:** - Experience with MLOps or LLMOps pipelines (e.g., MLflow, Kubeflow, SageMaker, Azure ML, Weights & Biases). - Knowledge of secure deployment, prompt injection prevention, and observability in Gen AI systems. - Familiarity with Open Source LLM hosting and model serving frameworks. In this role, you will have the opportunity to work on cutting-edge enterprise Gen AI and Agentic AI use cases, access the latest LLMs and tooling across major cloud ecosystems, and be part of a collaborative, innovation-driven environment within a leading AI Practice. Career development opportunities across AI research, product engineering, and applied solutions are also available to you. As a Gen AI Engineer with experience in AI/ML and at least 1 year in Gen AI or Agentic AI, you will play a critical role in designing, implementing, and scaling intelligent agents that autonomously plan, execute, and refine tasks across diverse enterprise use cases. Your hands-on experience with Gen AI, Agentic AI, Python, RAG Pipeline, and cloud platforms such as Azure or AWS will be key in shaping the future of autonomous AI solutions. **Key Responsibilities:** - Design and develop Agentic AI architectures using LLMs, planning modules, memory stores, and tool integration frameworks. - Implement task orchestration, decision reasoning, and self-reflection capabilities within autonomous agents. - Work with multi-agent workflows involving planning, tool use, dialogue, and adaptive learning loops. - Integrate and fine-tune LLMs (GPT, Llama, Claude, etc.) for use in agent workflows. - Collaborate with product and platform teams to deploy solutions on cloud environments (AWS, Azure, GCP). - Contribute to solution accelerators, reusable frameworks, and reference architectures for Gen AI use cases. - Optionally contribute to MLOps/LLMOps pipelines for continuous model integration, evaluation, and monitoring. **Required Skills & Experience:** - 4 years of experience in AI/ML/GenAI domains, with at least 1 year of experience in Generative AI. - Strong understanding of LLM architectures, Agentic AI, prompt engineering, RAG pipelines, and memory systems (e.g., vector DBs). - Experience in building agents using frameworks such as LangChain, AutoGen, CrewAI, Semantic Kernel, or similar. - Proficiency in Python and relevant Gen AI libraries (Transformers, HuggingFace, OpenAI API, etc.). - Experience deploying solutions on at least one cloud platform (AWS, Azure, or GCP). - Strong problem-solving skills and ability to work in a fast-paced, agile environment. - Strong Python programming skills. **Nice to Have:** - Experience with MLOps or LLMOps pipelines (e.g., MLflow, Kubeflow, SageMaker, Azure ML, Weights & Biases). - Knowledge of secure deployment, prompt injection prevention, and observability in Gen AI systems. - Familiarity with Open Source LLM hosting and model serving frameworks. In this role, you will have the opportunity to work on cutting-edge enterprise Gen AI and Agentic AI use cases, access the latest LLMs and tooling across major cloud ecosystem