AI Software Engineer

About the job

The AI Software Engineer designs, builds, and maintains software systems that enable development, deployment, and scaling of AI and machine learning solutions. This role focuses on production-grade engineering rather than pure research, working closely with Data Scientists, MLOps Engineers, and Platform teams to turn models into reliable, secure, and performant applications. The role bridges software engineering, ML systems, and cloud-native infrastructure to support enterprise AI use cases. Work Location: Bangalore Work Mode: Hybrid (3 Days in office) How You'll Create Impact Key Responsibilities AI Application & Systems Engineering • Design and develop AI-enabled applications and backend services • Integrate machine learning models into production systems via APIs and services • Build scalable, reliable, and testable software supporting AI workloads Model Integration & Deployment • Collaborate with Data Scientists to productionize ML models • Package and deploy models using containerized and cloud-native architectures • Support model serving, inference optimization, and versioning Platform, Performance & Reliability • Optimize systems for latency, throughput, and cost efficiency • Implement monitoring, logging, and alerting for AI services • Lead root-cause analysis for production issues involving AI systems Automation, CI/CD & DevOps • Build and maintain CI/CD pipelines for AI applications and services • Automate testing, deployment, and environment management • Ensure reproducibility and reliability across environments Collaboration & Engineering Excellence • Partner with MLOps, Data Engineering, and Cloud teams • Contribute to coding standards, documentation, and best practices • Support secure, compliant, and responsible AI deployment What Makes You Stand Out Technologies & Tools Programming & Software Engineering • Python (primary), with strong software engineering practices • RESTful API design and microservices architecture Machine Learning & AI (Integration Level) • ML frameworks: PyTorch, TensorFlow, or scikit-learn • Model serving frameworks (e.g., FastAPI, TorchServe, TF Serving) Experience integrating inference into applications • Cloud & Infrastructure • Cloud platforms: AWS, Azure, or GCP • Containerization: Docker • Orchestration: Kubernetes • Infrastructure as Code: Terraform, ARM/Bicep, or CloudFormation DevOps, CI/CD & Observability • CI/CD tools (GitHub Actions, GitLab CI, Azure DevOps, Jenkins) • Monitoring and logging (Prometheus, Grafana, ELK, or cloud-native tools) • Version control: Git Data & Systems • SQL and data access patterns • Message queues and streaming platforms (Kafka, cloud equivalents)