Project Domain : Banking & Financial Services

Required Tech Stack & Skills :

1. Agentic AI :

- AI Agents & Multi-Agent Systems, MCP Tools, RAG, Prompt Engineering, AI Safety, Evaluation Frameworks, Telemetry & Observability, and A2A Protocols

2. AI Platforms & Frameworks (Any 2) :

- Vertex AI, LangGraph, LangChain, Semantic Kernel, Google ADK, and Vector Databases

3. Cloud-Native Engineering :

- Strong hands-on experience with GCP, GKE, Cloud Run, Kubernetes, Microservices, and Event-Driven Architecture

4. Software Engineering :

- Python, FastAPI, API Development, CI/CD, Distributed Systems, and production-grade engineering practices

5. System Design & Architecture :

- Designing scalable, resilient, secure, and highly available AI/Cloud-native systems and distributed architectures

Key Responsibilities :

- Architect and implement complex Multi-Agent systems that orchestrate autonomous workflows to solve intricate business logic problems for our enterprise clients.

- Develop and optimize advanced RAG pipelines to ensure high-fidelity information retrieval and context-aware responses within production-grade AI applications.

- Integrate Model Context Protocol (MCP) standards to streamline communication between AI agents and diverse data sources, ensuring seamless interoperability across our ecosystem.

- Engineer robust, low-latency APIs using Python and FastAPI to serve AI models, ensuring high availability and performance for end-users.

- Deploy and manage scalable AI infrastructure on GCP, utilizing Kubernetes to orchestrate containerized workloads and ensure system reliability at scale.

- Lead the refinement of prompt engineering strategies to improve model reasoning, accuracy, and alignment with specific business objectives.

- Mentor junior engineering talent and foster a culture of technical excellence by conducting rigorous code reviews and architectural design sessions.