Job Description
Responsibilities: Take ownership of an existing AI-powered application through structured knowledge transfer, including understanding and documenting architecture, codebase, and integrations Provide ongoing application support including bug fixing, enhancements, incident resolution, performance tuning, and stability improvements Design, develop, and deploy end-to-end AI applications using LLMs, RAG, and agentic frameworks Build MCP-based tool integrations and agentic workflows to connect AI solutions with enterprise systems and processes Translate business requirements into scalable AI-powered solutions with robust backend and frontend design Develop and integrate APIs, application logic, and user-facing features for AI-driven use cases Implement monitoring, observability, and evaluation mechanisms for AI models, prompts, and agent behavior Collaborate with cross-functional stakeholders to deliver production-ready solutions aligned with business outcomes Contribute to application design, code quality, testing, CI/CD, and engineering best practices Requirements Bachelor’s degree in Computer Science or a related discipline Minimum 2 years of experience in full stack software engineering with production application exposure Proven experience building AI agents or multi-agent systems using frameworks like LangChain, LlamaIndex, LangGraph, AutoGen, or CrewAI Hands-on experience with LLMs, RAG architectures, agentic design patterns, and deploying generative AI applications Familiarity with MCP integrations, AI guardrails, application evaluation, and LLMOps practices Understanding of prompt engineering, model evaluation, AI observability tools (e.g., LangSmith), and responsible AI Solid software engineering skills including clean code, testing, design patterns, REST APIs, CI/CD, and containerization Proficiency in Python, TypeScript, React / Next.js, and Tailwind CSS Experience with relational and NoSQL databases, including schema design and query optimization Familiarity
