Seeking an experienced Lead Software Engineer to spearhead the design, development, and operation of enterprise AI platforms and intelligent automation solutions. This role focuses on Python engineering, cloud-native platforms, DevOps, and hands-on experience with Agentic AI, AI Copilots, RAG frameworks, and local LLM environments. Collaboration with Architecture, Security, Infrastructure, and Business teams is key to building secure, scalable, and governed AI solutions.

Key responsibilities include designing, developing, and operating enterprise AI platforms for Generative AI and Agentic AI use cases. Building AI-powered applications, designing multi-agent systems, and developing RAG solutions are crucial. You will also deploy and manage private/local AI environments, design reusable AI services and APIs, and collaborate with stakeholders to identify automation opportunities. Driving responsible AI, model governance, and security best practices is expected.

Further duties involve developing scalable backend services and REST APIs in Python, building cloud-native and on-premise solutions, and leading code reviews to ensure engineering standards are met. You will also build reusable software components, automation frameworks, and mentor development teams. DevOps responsibilities include designing and maintaining highly available infrastructure, driving Infrastructure as Code (IaC) practices, and building CI/CD pipelines. Implementing monitoring, logging, and observability mechanisms, supporting production environments, and participating in production support are also vital.

Requires 7+ years of software engineering experience with deep Python expertise and strong experience in designing and developing REST APIs and microservices. Proficiency in modern software engineering practices, design patterns, testing, and secure coding principles is essential.

Hands-on experience with Agentic AI solutions in enterprise environments and familiarity with frameworks like LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel are necessary. Experience building AI Agents, Multi-Agent Systems, AI Copilots, and Conversational AI Solutions is required, along with a strong understanding of Prompt Engineering, RAG Architectures, Tool Calling, and AI evaluation frameworks.

Experience with private/local LLM environments using tools like Ollama, vLLM, or Hugging Face Transformers is crucial, including deploying open-source models and understanding GPU-enabled infrastructure, model optimization, and AI platform security.

Skills in data and AI platforms, including Vector Databases (Pinecone, ChromaDB, Qdrant) and embedding models, are important. Strong experience with cloud platforms (AWS/Azure), Docker, Kubernetes, Terraform, Ansible, and CI/CD tools (GitHub Actions, Azure DevOps) is required. Working knowledge of monitoring, logging, cloud security, and infrastructure automation is also necessary.

Preferred qualifications include experience in banking/financial services and exposure to AI governance and regulatory compliance. A Bachelor's or Master's Degree in a relevant field and a minimum of 2 years of hands-on experience delivering Generative AI or Agentic AI solutions are also expected.