G60 - Full Stack Engineer

Role Summary We are seeking a Software Developer with full stack experience to design, build, and deploy generative AI applications and agentic workflows that automate complex business processes and power intelligent user experiences. Key Responsibilities Design and implement end-to-end GenAI applications (chatbots, copilots, workflow assistants) across web and backend services. Build agentic workflows that orchestrate multi-step tasks using LLMs, tools, APIs, and event-driven triggers. Develop and maintain backend services and APIs that integrate GenAI models, vector stores, and enterprise data sources. Implement retrieval-augmented generation (RAG) pipelines and tool-calling agents for knowledge and process automation. Build responsive front-end interfaces (web and/or internal consoles) for interacting with agents and monitoring workflows. Deploy, monitor, and optimize GenAI and agentic services in production (performance, reliability, cost, safety). Implement observability, logging, and evaluation frameworks for agent behavior, prompts, workflows, and user journeys. Collaborate with product managers, data/ML engineers, and domain experts to translate business requirements into GenAI-powered solutions. Ensure security, guardrails, and governance for AI features, including access control, PII handling, and safe outputs. Contribute to internal SDKs, templates, and reusable components to accelerate GenAI and agentic development across teams. Required Experience & Skills 3–5 years of professional software development experience with a strong full stack background. Proficiency in at least one modern backend stack (e.g. Node.js/TypeScript, Python, Java, or Go) and REST/gRPC API design. Experience with modern front-end frameworks (e.g. React, Vue, or Angular) and building production-grade UIs. Hands-on experience integrating LLMs/GenAI APIs (e.g. OpenAI, Anthropic, Azure/GCP/AWS AI services, or open-source models). Familiarity with agent frameworks and orchestration libraries (e.g. LangChain, LlamaIndex, AutoGen, custom tool-calling frameworks). Experience with databases (SQL/NoSQL) and ideally vector databases or embedding-based search. Solid understanding of software engineering best practices: testing, CI/CD, code reviews, and secure coding. Nice-to-Have Experience designing multi-agent systems, complex workflow graphs, or event-driven architectures. Exposure to RAG architectures, prompt engineering, and evaluation of LLM outputs. Experience with containers and cloud deployment (Docker, Kubernetes, serverless platforms). Background working with data/ML teams or on ML/LLM-backed products.