Senior AI Engineer

Key Responsibilities :

- Design, develop, and deploy AI Agents and Multi-Agent Systems for enterprise use cases.

- Architect agentic AI solutions using frameworks such as LangGraph, LangChain, Semantic Kernel, and Google ADK.

- Design and implement Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise data sources.

- Develop effective prompt-engineering strategies and implement AI safety and responsible AI practices.

- Design evaluation frameworks to measure agent accuracy, reliability, performance, and safety.

- Implement telemetry, observability, monitoring, and tracing for AI-agent systems.

- Work with MCP tools and A2A protocols to enable agent-to-tool and agent-to-agent interactions.

- Build scalable AI applications using Vertex AI and other GCP services.

- Develop cloud-native applications using GKE, Cloud Run, Kubernetes, microservices, and event-driven architectures.

- Design and develop APIs and backend services using Python and FastAPI.

- Implement CI/CD pipelines and engineering best practices for AI applications.

- Design distributed systems that are scalable, resilient, secure, and highly available.

- Collaborate with architects, data scientists, software engineers, and business stakeholders to translate requirements into production-ready AI solutions.

Required Skills & Experience :

- 814 years of overall technology/software engineering experience.

- Strong hands-on experience with :

i. AI Agents and Multi-Agent Systems

ii. MCP Tools

iii. Retrieval-Augmented Generation (RAG)

iv. Prompt Engineering

v. AI Safety

vi. AI Evaluation Frameworks

vii. Telemetry & Observability

viii. A2A Protocols

AI Platforms & Frameworks :

- Vertex AI

- LangGraph

- LangChain

- Semantic Kernel

- Google ADK

- Vector Databases

Cloud-Native Engineering :

- Strong hands-on experience with Google Cloud Platform (GCP)

- Google Kubernetes Engine (GKE)

- Cloud Run

- Kubernetes

- Microservices architecture

- Event-driven architecture

- Distributed systems

Software Engineering :

- Strong proficiency in Python

- FastAPI and API development

- CI/CD and DevOps practices

- Strong understanding of system design and scalable architecture

- Experience designing production-grade, resilient, and secure applications

Preferred Candidate Profile :

- Strong understanding of enterprise GenAI and Agentic AI architecture.

- Experience taking AI-agent solutions from proof of concept to production.

- Strong architectural and system-design capabilities.

- Experience with enterprise-scale cloud-native applications.

- Strong problem-solving, communication, and cross-functional collaboration skills.

Skills

Generative AI, Agentic AI, RAG, LangChain, VectorDB, Python, Google Cloud Platform, Vertex AI