App Store Developers

Job Description – App Store Developers |1-4 Years of Experience

About The Role

• We’re looking for a Full-Stack Agent Developer to design, build, and productionize AI agents on top of Lyzr – our full-stack agent platform.
• You’ll primarily build agents using Lyzr, but you must bring prior hands-on experience with frameworks like LangChain, OpenAI Agent SDK, Google ADK, etc. so you can compare, port, and improve on what the broader ecosystem offers.
  
  

• Design and implement AI agents on Lyzr, leveraging its native capabilities for orchestration, tooling, and governance.
• Use your experience with LangChain / OpenAI Agent SDK / Google ADK / other frameworks to:
• Translate patterns and best practices into Lyzr-native agents
• Help migrate/port existing agents from those frameworks into Lyzr
• Build and optimize RAG (Retrieval-Augmented Generation) pipelines: data ingestion, indexing, retrieval, and response assembly.
• Do hands-on context engineering: system prompts, tool definitions, multi-step workflows, and evaluation/iteration loops.
• Develop full-stack features around agents using TypeScript (Node.js backends + modern frontend framework such as React/Next.js).
• Deploy and operate services on AWS (Lambda/ECS/EKS, API Gateway, S3, RDS/DynamoDB, CloudWatch, IAM, etc.).
• Integrate Lyzr agents with external systems and APIs (CRMs, data warehouses, internal tools).
  
  

• 1–4+ years of professional software development experience (or equivalent depth via projects/startups).
• Strong full-stack engineering skills + agentic AI experience with production
• Hands-on experience with at least one major agent framework, such as: LangChain, OpenAI Agent SDK, Google ADK (Bonus: CrewAI, LangGraph, AutoGen, etc.)
• Strong desire and ability to build new agents primarily on Lyzr, and to map concepts from other frameworks into Lyzr.
• Proven experience with RAG: Vector databases (e.g., Pinecone, Weaviate, pgvector, OpenSearch, etc.), Chunking strategies, embeddings, retrieval optimization
• Solid context engineering skills:
• System / tool / user prompt design
• Multi-step agent workflows and planning
• Solid experience building and deploying production systems on AWS.
• Strong engineering fundamentals: testing, Git, CI/CD, code reviews, API design.
  
  

• Experience with more than one agent framework and migrations between them.
• Experience with LLM evaluation, observability, and guardrail frameworks.
• Familiarity with enterprise data ecosystems (Snowflake, BigQuery, Salesforce, etc.).
• Open-source contributions or public demos of agents you’ve built.