Full Stack Developer

Job Description

We are looking for a Full Stack Developer – Agentic Systems to build the product layer for AI-native workflows by turning LLMs, agents, memory, and external tools into reliable, production-grade user experiences. 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 behaviour 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 behaviour, evaluation results, and observed failure modes
• Establish reusable patterns for building scalable agentic systems.

Requirements

Education:

Relevant degree in Computer Science, Information Technology, Electronics, or a related field.

Technical Skills (Must haves):

• Next.js, Node.js, Python
• SQL and NoSQL databases
• API design and backend architecture
• Docker
• 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 such as LangChain, LlamaIndex, CrewAI, AutoGen, or similar
• Vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector
• Observability, reliability, fallback handling, and debugging in production
• Evaluation frameworks for LLM or agent performance
• Workflow orchestration systems

Need to have (Can be bridged):

• Strong understanding of system design, APIs, and production-grade architecture
• Ability to work through ambiguity and make pragmatic engineering decisions
• Strong ownership mindset with experience taking features from idea to production

Good to have (Not essential):

• Familiarity with prompt engineering, retrieval strategies, and context management
• Experience building AI products beyond chat-based interfaces

Preferred Qualifications (Optional):

Experience collaborating with ML, backend, product, and design teams to ship AI-native features from concept to production.