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# Backend AI Engineer

- **Company:** AllSTEM Connections
- **Location:** Ontario, CA, United States
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon S3, Cloud Engineering, Software Quality, Data Governance, Extract Transform Load (ETL), Distributed Computing Environment, Distributed Systems, Django Web Framework, Python (Programming Language), Machine Learning, NoSQL, SQL Databases, Stata, Data Streaming, Web Application Frameworks, Software Organization, Data Storage Technologies, Flask (Web Framework), Large Language Models, Grafana, Backend, Cloudformation, Fastapi, Event Driven Architecture, Build Management, Containerization, Pyspark, Kubernetes, Information Technology, Apache Kafka, Graphql, Machine Learning Operations, Asynchronous Programming, Functional Programming, Api Design, Api Gateway, Restful APIs, Terraform, Data Pipelines, Docker, Databricks, Microservices
- **Published:** June 24, 2026
- **Apply:** https://www.dice.com/job-detail/632d3633-9c20-4835-8fc8-85aaab3a093d

## About the Role

We are seeking a highly skilled Backend Engineer to design, build, and scale AI-powered services. This role will focus on developing robust backend systems that support LLM-driven applications, integrating machine learning models, and delivering secure, high-performance APIs in a cloud-native environment., Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience)
4+ years of backend development experience, including 2+ years working with AI/ML or LLM-based systems
Strong proficiency in Python and API development
Hands-on experience with AWS services (Lambda, API Gateway, S3, SageMaker, Bedrock)
Experience with both SQL and NoSQL databases; familiarity with vector databases preferred
Knowledge of asynchronous programming, event-driven architectures, and distributed systems
Solid understanding of testing frameworks and software development best practices

Preferred Qualifications:

Experience with PySpark, Databricks, or distributed data processing frameworks
Familiarity with MLOps tools (MLflow, Kubeflow, SageMaker Pipelines) and Infrastructure as Code (Terraform, CloudFormation)
Experience with streaming platforms (Kafka, Kinesis) and ETL/data pipeline development
Knowledge of microservices architecture, observability tools, and GraphQL APIs
Background in regulated industries and understanding of data governance and compliance
Exposure to statistical modeling tools such as R or Stata

## Description

Design and develop scalable backend services using Python frameworks such as FastAPI, Flask, or Django
Build and deploy AI/ML and LLM-powered applications, including prompt orchestration and RAG architectures
Develop and maintain secure, high-performing RESTful APIs
Deploy and manage ML models in production environments
Architect and optimize data storage solutions across relational, NoSQL, and vector databases
Implement containerized solutions using Docker and cloud-native orchestration tools (ECS/EKS)
Collaborate with cross-functional teams to translate business needs into technical solutions
Ensure code quality through testing, automation, and best engineering practices


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