Backend AI Engineer

FASTRA LLC
New York, NY, United States
5 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
10 years minimum
Working hours
Regular working hours
Job source

Tech stack

JavaScript (Programming Language) Application Programming Interfaces (APIs) Artificial Intelligence Airflow Amazon Web Services Microsoft Azure Cloud Computing Software Quality Databases Continuous Delivery Continuous Integration DevOps
+30 more
Distributed Systems PostgreSQL Machine Learning MongoDB Node.Js NoSQL Redis SQL Databases TypeScript Web Services Google Cloud Cloud Platform System Large Language Models Multi-Agent Systems Backend Git Event Driven Architecture Containerization Kubernetes Information Technology Deployment Automation Apache Kafka Front End Software Development Stream Processing Software Version Control Data Pipelines Api Management Docker Golang Microservices

Job description

Senior Backend Engineer will be responsible for designing, developing, and maintaining scalable and efficient backend systems that power our applications. The ideal candidate will have a strong background in backend development, solid understanding of distributed systems, LLM systems, agentic workflows and experience with cloud technologies. Core engineering stack Languages: NodeJS, TypeScript, Go APIs and services: REST, microservices Cloud and infrastructure: AWS and/or Google Cloud Platform, Kubernetes Distributed systems: event-driven architectures, including Kafka Orchestration Frameworks: LangGraph, LangChain, AirFlow, etc Responsibilities: Design, develop, and maintain high-performance backend services and APIs. Collaborate with cross-functional teams to gather requirements, architect solutions, and implement features that meet business needs. Optimize backend systems for performance, scalability, and reliability, ensuring smooth operation under high load. Drive technical direction for agentic AI initiatives, influencing architecture patterns, autonomy boundaries, and system design. Design, build, and operate production-grade agentic AI systems used across multiple products. Own and evolve shared agentic AI capabilities, including: Agent frameworks and orchestration layers, Planning, tool use, and memory strategies Retrieval and grounding (RAG) pipelines LLM infrastructure, inference, and model gateways Evaluation, observability, and safety tooling for autonomous systems Lead technical design reviews and help teams navigate tradeoffs involving autonomy, safety, reliability, scalability, and cost. Implement best practices for code quality, testing, and deployment automation. Work closely with DevOps teams to deploy and manage backend services in cloud environments (e.g., AWS, Azure, Google Cloud). Collaborate with frontend engineers to define API contracts and ensure seamless integration between frontend and backend systems. Communicate effectively with team members, stakeholders, and management to provide project updates, status reports, and technical recommendations.

Requirements

Bachelor s degree in computer science, Engineering, or related field. Master’s degree preferred. 10+ years of experience building large-scale distributed systems with a strong proficiency in NodeJS/Javascript/Typescript, and/or Golang. Strong experience with LLM systems, agentic workflows or advanced ML infrastructure Proven ownership of complex, cross-cutting agentic systems spanning multiple teams or products. Strong engineering fundamentals across backend systems, APIs, data pipelines, and cloud infrastructure. Deep experience across the agentic AI stack, including planning, tool use, memory, and evaluation. Fluency with AI-assisted and agentic development workflows. Comfort operating in ambiguous problem spaces and translating them into shipped, reliable autonomous systems. Ability to influence technical direction and align teams without formal authority. Experience in workflow engines, async processing, queues, and streaming systems. Experience with cloud platforms and services and containerization technologies (e.g., Docker, Kubernetes). Proficiency in database technologies such as SQL (e.g. PostgreSQL) and NoSQL (e.g. MongoDB, Redis). Strong problem-solving skills, with the ability to analyze complex technical challenges and propose effective solutions. Experience with version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) pipelines. Excellent communication skills, with the ability to collaborate effectively with cross-functional teams and stakeholders.

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