Overview

Job : AI Software Engineer (Generative AI & Cloud-Native Systems)

Responsibilities

• Design, build, and optimize cloud-native backend services (Python/Node.js) for AI applications on AWS or Azure (e.g., serverless, containers, managed services).
• Develop infrastructure as code (IaC) using Terraform, CloudFormation, or ARM
• Implement CI/CD pipelines for AI model deployment, application updates, and automated testing (e.g., GitHub Actions, Azure DevOps).
• Build scalable APIs/microservices (FastAPI, gRPC) to serve AI features (e.g., LLM inference, agent workflows) with security, latency, and cost efficiency. Ensure reliability and observability via monitoring (Prometheus, CloudWatch), logging, and alerting for AI systems.
• Integrate generative AI and agentic systems(e.g., LangChain, CrewAI, and AutoGen) into full-stack applications—not just prototyping, but productionizing workflows.
• Design RAG pipelines with vector databases (e.g.,Azure Cognitive Search,AWS
• Build data pipelines forAI training/inference (ingestion, preprocessing, synthetic data) with cloud tools (e.g., AWS Glue, Azure Data Factory). Collaborate with ML engineers to deploy modelsvia TorchServe, Triton, or cloud-managed services (e.g., SageMakerEndpoints, Azure ML Endpoints).
• Work cross-functionally withproduct, frontend, and data teamsto translate business needs into scalable AI solutions.

Qualifications

• About the Role
• If youthrive in environments where AI meansbuilding robust, maintainable systems (not just notebooks), and you've shippedAI features in production using AWS/Azure, this role is for you.
• 3–4 years of professional software development experience with strong fundamentals:
• Proficiency in Python (required) and modern frameworks (FastAPI, Flask, Django).
• Compute (EC2, Lambda, Azure Functions, VMs) Storage (S3, Blob Storage)
• Databases (RDS, Cosmos DB, DynamoDB)
• API gateways (API Gateway, AzureAPI Management)
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
• Proven track recordin CI/CD pipelines, infrastructure-as-code (Terraform/CloudFormation), and monitoring tools.
• 1–2 years of hands-on experience in AI application development, specifically:
• Building generative AI or agenticworkflows (e.g., using LangChain, CrewAI, AutoGen).
• Implementing RAG pipelines or fine-tuning LLMs in production (e.g.,via
• AWS Bedrock, Azure OpenAI, or open-source models).
• Strong software engineering discipline:
• Writing testable, maintainable code with unit/integration tests.
• Understanding of system design (scalability, security, cost optimization).
• Bachelor's or Master's in Computer Science, Software Engineering, or a related field.
• Knowledge of serverless architectures (AWS Lambda/Azure Functions) for AI workloads.
• Familiarity with MLOps tools(MLflow, Kubeflow) or cloud-native MLOps (SageMaker Pipelines, Azure ML Pipelines).
• Prior work on cost-optimized AI systems (e.g., model quantization, auto- scaling, spot instances).
• Contributionsto open-source AI/ML projects or cloud infrastructure tooling.
• WhyThis Role
• You'll build real-world AI products, not just research prototypes—your work directly impacts users.
• We prioritize clean code, infrastructure as code, and observability over magic model hype.
• You'll work with modern cloud tools (AWS/Azure) in a team that values software engineering rigor as much as AI innovation.