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.