Join our dynamic team as a Senior AI Engineer focused on Creative Technology, specializing in Automation, AI, Machine Learning, and Robotic Process Automation (RPA). You will be instrumental in architecting and delivering cutting-edge enterprise-grade AI solutions, with a significant emphasis on Generative AI (GenAI), agent-based systems, and Large Language Model (LLM) orchestration.

This role offers the opportunity to own the technical roadmap, champion engineering best practices, and act as a thought leader. You will drive the implementation of scalable, secure, and efficient AI workflows, fostering innovation and elevating our applied AI capabilities.

Lead the end-to-end technical design and successful delivery of GenAI and agentic systems for both internal and external applications. Architect sophisticated multi-agent workflows utilizing frameworks like LangChain and A2A protocols, alongside custom orchestration solutions. Guide the design and fine-tuning of prompt architectures, context strategies (including MCP), and hybrid Retrieval-Augmented Generation (RAG) pipelines.

Integrate AI services seamlessly into enterprise platforms such as Azure Foundry, Databricks, and core business systems. Mentor and lead engineering pods, ensuring technical alignment across product and platform teams. Drive research-backed optimization to enhance performance, reduce latency, and improve accuracy. Establish reusable templates, shared components, and internal GenAI SDKs, while strictly enforcing standards for ethical AI, context control, prompt security, and hallucination mitigation.

We seek candidates with a minimum of 4 years in AI/ML/GenAI solutioning, including at least 2 years in a technical leadership capacity. A deep proficiency in Python 3 is essential, with a strong command of libraries such as openai, pydantic, transformers, faiss, and langchain. Demonstrated experience in deploying scalable GenAI solutions using cloud-native design principles is a must.

Strong working knowledge of Azure cloud services, GitHub workflows, and CI/CD best practices is required. Experience in vector store optimization, token-level control, and effective prompt performance management is also a key requirement. While a Bachelor's degree is expected, specific fields like B.Tech are commonly seen in successful candidates.