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.