MulticoreWare - Full Stack Developer - Agentic Systems

Job Description :

We are looking for a Full Stack Developer: Agentic Systems to build the product layer for AI-native workflows.

This role focuses on turning LLMs, agents, memory, and external tools into reliable, production-grade user experiences. You will design and ship systems where agents can plan, execute multi-step tasks, recover from failures, maintain context, and deliver consistent value across sessions.

Responsibilities :

- Build end-to-end product features across frontend, backend, and AI integrations

- Design agent workflows that support planning, tool use, failure handling, and recovery

- Integrate LLMs, memory, RAG systems, and external tools into production systems

- Build real-time AI interactions using streaming, partial results, and low-latency responses

- Improve reliability, observability, fallback logic, and production behavior of AI workflows

- Collaborate with ML, backend, product, and design teams to ship features from concept to production

- Iterate on AI workflows based on user behavior, evaluation results, and observed failure modes

- Establish reusable patterns for building scalable agentic Strong full stack engineering experience across frontend and backend development

- Solid understanding of system design, APIs, and production-grade architecture

- Experience building with LLMs, RAG systems, agents, or AI-powered applications

- Ability to work through ambiguity and make pragmatic engineering decisions

- Strong ownership mindset with experience taking features from idea to production

Tech Stack & Skills:

Core Engineering:

- Next.js, Node.js, Python

- SQL and NoSQL databases

- API design and backend architecture

- Docker

AI & Agentic Systems:

- LLM integration using OpenAI, Anthropic, or open-source models

- Agent workflows, tool use, memory, or RAG-based systems

- Streaming responses and real-time AI interaction patterns

- Agent frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, or similar

- Vector databases: Pinecone, Weaviate, Qdrant, Milvus, or pgvector

Production & Reliability:

- Observability, reliability, fallback handling, and debugging in production

- Experience with evaluation frameworks for LLM or agent performance

- Experience with workflow orchestration systems

Nice to Have:

- Familiarity with prompt engineering, retrieval strategies, and context management

- Experience building AI products beyond chat-based interfaces

(ref:hirist.tech)