Full Stack Engineer (Java and Python AI)

We are seeking an engineer who is exceptional in Java Springboot, experienced with web frameworks (Flask, FastAPI, Django), comfortable building frontends (React, Angular), and capable of delivering production-grade GenAI solutions (LLMs, RAG, Agents/Agentic AI, MCP, guardrails). Candidate should be able to design, build, and operate services and applications that leverage modern AI patterns with strong software craftsmanship.

What you'll do

• Develop Backend & APIs – Build backend services using Spring Boot and Python (Flask/FastAPI/Django), including APIs, background jobs, and integrations.
• Develop & Maintain GenAI Solutions – Design and implement GenAI features, RAG pipelines, prompts, tools, memories, and agentic workflows.
• Ensure Quality & Production Readiness – Own testing using PyTest, integration/contract tests, and CI automation, while implementing AI guardrails, observability, and fallback strategies.
• Build Full-Stack & Data Solutions – Develop React/Angular UIs and work with vector stores, embeddings, caching, and secure data connectors, while collaborating with product/design/platform teams and mentoring engineers.

What you'll bring

• 10+ years of software engineering experience in designing, developing, troubleshooting, and supporting large, complex systems.
• Strong Java/Spring Boot Microservices expertise – 5+ years of microservices development and 3+ years of Spring Boot, with hands-on experience in REST/JSON APIs and object-oriented design.
• Advanced Python backend development – 5+ years of Python experience, including Flask/ FastAPI/ Django, API design, asynchronous programming, testing, packaging, and performance optimization.
• GenAI / Agentic AI experience – At least 1 year of hands‑on experience with LLMs, RAG, agentic workflows, tool/function calling, MCP, embeddings, guardrails, and frameworks such as LangGraph/LangChain or CrewAI.
• Full-stack, cloud, and DevOps capabilities – 3+ years with React or Angular, plus experience with Docker, Kubernetes, CI/CD, Kafka, databases/vector DBs, Redis, cloud infrastructure, and observability.

AI Fluency Expected

At Myridius, we expect AI fluency across all roles. This means the ability to effectively and responsibly use AI-enabled tools to improve productivity, quality, analysis, decision-making, documentation, testing, delivery, and problem-solving within one's function. All candidates should be comfortable working with AI as a day-to-day enabler while applying sound judgment, critical thinking, and accountability for outcomes.

Good to Have

Experience with LLM observability and Guardrails, as well as hands‑on knowledge of vector and graph databases to support the development, monitoring, and optimization of AI-powered applications.