AI Software Engineer

What You Can Expect

Job Summary

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)

Your Background

Preferred Qualifications

• 6+ years total engineering experience
• Experience with real-time or low-latency inference systems
• Experience working with MLOps pipelines and model lifecycle tools
• Experience in enterprise or regulated environments
• Cloud or AI-related certifications (preferred)

Core Competencies

• Strong software engineering fundamentals
• Systems thinking and performance optimization
• Ability to collaborate across research and engineering teams
• Clear communication and documentation skills
• Ownership mindset for production reliability

Physical Requirements

Travel Expectations