AI Software Engineer

Job Title: AI Software Engineer (Generative AI & Cloud-Native Systems)
Location: Hybrid (Bangalore)
Experience: 3–4 years in software development + 1–2 years in AI (agentic/generative)
About the Role
We’re building scalable, production-grade full-stack AI applications where AI capabilities (like generative models and agentic workflows) are deeply integrated into user-facing products. As an AI Software Engineer, you’ll be the bridge between AI research and real-world systems: you’ll design, build, and maintain cloud-native backend services, infrastructure, and APIs that power AI features—not just train models, but ensure they’re reliable, secure, and cost-efficient at scale. Your core focus will be software engineering excellence (clean code, testing, CI/CD, system design) with AI as a component, not the sole focus.
If you thrive in environments where "AI" means building robust, maintainable systems (not just notebooks), and you’ve shipped AI features in production using AWS/Azure, this role is for you.
Key Responsibilities
Software Engineering & Cloud Infrastructure (Primary Focus)
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 templates to automate cloud deployments.
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.
AI Integration & Productionization (Secondary Focus)
Integrate generative AI and agentic systems (e.g., LangChain, CrewAI, AutoGen) into full-stack applications—not just prototyping, but productionizing workflows.
Design RAG pipelines with vector databases (e.g., Azure Cognitive Search, AWS OpenSearch) and optimize for latency/cost.
Fine-tune LLMs (using LoRA, PEFT) or leverage cloud AI services (e.g., AWS Bedrock, Azure OpenAI) for custom use cases.
Build data pipelines for AI training/inference (ingestion, preprocessing, synthetic data) with cloud tools (e.g., AWS Glue, Azure Data Factory).
Collaborate with ML engineers to deploy models via TorchServe, Triton, or cloud-managed services (e.g., SageMaker Endpoints, Azure ML Endpoints).
Collaboration & Ownership
Work cross-functionally with product, frontend, and data teams to translate business needs into scalable AI solutions.
Champion software best practices: testing (unit/integration), code reviews, documentation, and modular design.
Mentor junior engineers on cloud engineering and AI system design.
Minimum Qualifications
3–4 years of professional software development experience with strong fundamentals:
Proficiency in Python (required) and modern frameworks (FastAPI, Flask, Django).
Experience building cloud-native backend systems (AWS or Azure) with services like:
Compute (EC2, Lambda, Azure Functions, VMs)
Storage (S3, Blob Storage)
Databases (RDS, Cosmos DB, DynamoDB)
API gateways (API Gateway, Azure API Management)
Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
Proven track record in 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 agentic workflows (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).
Experience with cloud AI services (SageMaker, Azure ML) or deploying open-source models on cloud infrastructure.
Strong software engineering discipline:
Writing testable, maintainable code with unit/integration tests.
Experience with Git workflows, agile development, and collaborative code reviews.
Understanding of system design (scalability, security, cost optimization).
Bachelor’s or Master’s in Computer Science, Software Engineering, or related field.
Preferred Qualifications
Experience with full-stack development (frontend frameworks like React/Vue for AI-powered UIs).
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, autoscaling, spot instances).
Contributions to open-source AI/ML projects or cloud infrastructure tooling.
Why This 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.

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