Full Stack Developer: Agentic Systems

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.