Agentic AI Developer

Job description:

Key Responsibilities:

• Design, develop, and deploy Generative AI applications and Agentic AI solutions.
• Build intelligent agents capable of executing multi-step tasks, using tools, and coordinating workflows.
• Implement agent harnessing concepts, including tool integration, context management, orchestration, execution control, and error handling.
• Develop scalable, production-ready backend services and APIs using Python and FastAPI.
• Integrate LLMs, external APIs, enterprise data sources, and business applications into agent-based systems.
• Design and implement reliable agent deployment workflows, including monitoring, logging, testing, and performance optimization.
• Build scalable and resilient systems to support production-grade AI applications.
• Optimize AI application performance, latency, reliability, and resource utilization.
• Leverage GCP services for application deployment, infrastructure, and AI solution development, where applicable.
• Collaborate with cross-functional teams to translate business requirements into AI-powered solutions.
• Participate in Agile ceremonies, sprint planning, code reviews, and continuous improvement initiatives.

Must-Have Skills:

• Python: Minimum 8 years of strong hands-on development experience.
• Generative AI: At least 4 years of experience working with GenAI technologies and LLM-based applications.
• Agentic AI: 2-3 years of hands-on experience developing and implementing agent-based solutions.
• Agent Deployment: Experience deploying, operating, and maintaining AI agents in production environments.
• Agent Harnessing: Strong understanding of agent orchestration, tool calling, context handling, execution management, and guardrails.
• FastAPI: Hands-on experience developing scalable REST APIs and backend services.
• Scalable Systems: Experience designing reliable, maintainable, and high-performance systems.
• Agile: Experience working in Agile development environments.

Good-to-Have Skills:

• Experience with Google Cloud Platform (GCP) and cloud-native application deployment.
• Familiarity with agent orchestration frameworks such as LangGraph, CrewAI, or similar technologies.
• Experience with RAG, vector databases, embeddings, and enterprise knowledge retrieval.
• Knowledge of LLM evaluation, observability, security, and production monitoring.
• Experience with Docker, Kubernetes, and CI/CD pipelines.
• Familiarity with multi-agent systems and enterprise AI integrations.

Skills

AI Agents, Agent Orchestration, Agentic AI, Gen AI, Generative AI, LLM, Python