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We are seeking an experienced Senior Backend Engineer to design, develop, and maintain the infrastructure powering our generative AI applications. You will work closely with AI engineers, platform teams, and product stakeholders to build scalable, reliable backend systems that support AI model deployment, inference, and integration. This role combines traditional backend engineering expertise with cutting-edge AI infrastructure challenges to deliver robust solutions at enterprise scale.
Job Responsibility:
Design and implement scalable backend services and APIs for generative AI applications using microservices architecture and cloud-native patterns
Build and maintain model serving infrastructure with load balancing, auto-scaling, caching, and failover capabilities for high-availability AI services
Deploy and orchestrate containerized AI workloads using Docker, Kubernetes, ECS, and OpenShift across development, staging, and production environments
Develop serverless AI functions using AWS Lambda, ECS Fargate, and other cloud services for scalable, cost-effective inference
Implement robust CI/CD pipelines for automated deployment of AI services, including model versioning and gradual rollout strategies
Create comprehensive monitoring, logging, and alerting systems for AI service performance, reliability, and cost optimization
Integrate with various LLM APIs (OpenAI, Anthropic, Google) and open-source models, implementing efficient batching and optimization techniques
Build data pipelines for training data preparation, model fine-tuning workflows, and real-time streaming capabilities
Ensure adherence to security best practices, including authentication, authorization, API rate limiting, and data encryption
Collaborate with AI researchers and product teams to translate AI capabilities into production-ready backend services
Requirements:
Bachelor’s degree in computer science, Engineering, or related technical field, or equivalent practical experience
4–6 years of experience in backend engineering with focus on scalable, production systems
2+ years of hands-on experience with containerization, Kubernetes, and cloud infrastructure in production environments
Demonstrated experience with AI/ML model deployment and serving in production systems
Strong experience with backend development using Python, with familiarity in Go, Node.js, or Java for building scalable web services and APIs
Hands-on experience with containerization using Docker and orchestration platforms including Kubernetes, OpenShift, and AWS ECS in production environments
Proficient with cloud infrastructure, particularly AWS services (Lambda, ECS, EKS, S3, RDS, ElastiCache) and serverless architectures
Experience with CI/CD pipelines using Jenkins, GitLab CI, GitHub Actions, or similar tools, including Infrastructure as Code with Terraform or CloudFormation
Strong knowledge of databases including PostgreSQL, MongoDB, Redis, and experience with vector databases for AI applications
Familiarity with message queues (RabbitMQ, Apache Kafka, AWS SQS/SNS) and event-driven architectures
Experience with monitoring and observability tools such as Prometheus, Grafana, DataDog, or equivalent platforms
Knowledge of AI/ML model serving frameworks like MLflow, Kubeflow, TensorFlow Serving, or Triton Inference Server
Understanding of API design principles, load balancing, caching strategies, and performance optimization techniques
Experience with microservices architecture, distributed systems, and handling high-traffic, low-latency applications