We are seeking a highly skilled and self-motivated AI Backend Engineer to join our growing R&D team. In this role, you will play a key part in the design, development, and deployment of our next-generation backend services, AI-driven workflows, and GenAI applications.
We are looking for a seasoned engineer who is passionate about building robust, scalable systems and has a proven track record of integrating cutting-edge LLM technologies into production. You will work closely with architects and product teams to turn complex AI concepts into reliable, high-performance backend solutions.
Key Responsibilities
Feature Development: Design and build high-performance, resilient, and scalable backend services using Python.
AI Implementation: Integrate LLMs using AWS Bedrock and other GenAI frameworks to create innovative, AI-powered solutions.
Workflow Orchestration: Design and implement complex, end-to-end event-driven architectures utilizing AWS Step Functions.
System Architecture: Contribute to the design of distributed systems, ensuring they meet high standards for performance and scalability.
Code Quality: Write clean, maintainable, and well-tested code, participating in code reviews to ensure best practices across the stack.
Collaboration: Partner with Product, Data Science, and DevOps teams to translate business requirements into technical implementations.
Problem Solving: Maintain a hands-on approach to troubleshooting, optimizing, and technical problem-solving within the GenAI ecosystem.
Requirements: Backend Expertise: 5+ years of experience in backend software engineering, with at least 3 years of hands-on experience with AWS services.
Python Proficiency: Expert-level Python skills for building complex backend applications.
GenAI & RAG: Practical experience with Retrieval-Augmented Generation (RAG) frameworks and LLM orchestration.
AI-Driven Workflows: Proven experience building systems that combine structured logic with LLM reasoning.
AWS Bedrock: Direct experience integrating and fine-tuning LLM interactions via AWS Bedrock.
Core AWS Stack: Strong knowledge of Lambda, API Gateway, DynamoDB, Aurora/Postgres, and S3.
Scalability: Proven ability to develop and maintain large-scale, high-availability systems.
Event-Driven Architecture: Experience with Kafka or other high-throughput event-streaming systems.
Orchestration Tools: Deep expertise with AWS Step Functions.
Observability: Proficiency with AWS monitoring tools like CloudWatch and X-Ray.
Modern Infrastructure: Experience with Docker, Kubernetes (EKS), and microservices environments.
Domain Experience: Experience in production-grade systems within fintech, enterprise SaaS, or other regulated industries.
This position is open to all candidates.