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):