Algonomy - AI Software Engineer - Generative AI & Cloud Native Systems

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 :

Were 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, youll be the bridge between AI research and real-world systems: youll design, build, and maintain cloud-native backend services, infrastructure, and APIs that power AI featuresnot just train models, but ensure theyre 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 youve 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, and AutoGen) into full-stack applicationsnot 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 forAI 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., SageMakerEndpoints, 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

- Proficiency in Python (required) and modern frameworks (FastAPI, Flask, Django).

- Experience building cloud-native backend systems (AWS or Azure) with services like :

1. Compute (EC2, Lambda, Azure Functions, VMs) Storage (S3, Blob Storage).

2. Databases (RDS, Cosmos DB, DynamoDB).

3. API gateways (API Gateway, AzureAPI Management).

- Hands-on experience with containerization (Docker) and orchestration (Kubernetes).

- Proven track recording CI/CD pipelines, infrastructure-as-code (Terraform/CloudFormation), and monitoring tools.

- 1- 2 years of hands-on experience in AI application development

- 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).

- Bachelors or Masters in Computer Science, Software Engineering, or a 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.

WhyThis Role?

- Youll build real-world AI products, not just research prototypesyour work directly impacts users.

- We prioritize clean code, infrastructure as code, and observability over "magic model" hype.

- Youll work with modern cloud tools (AWS/Azure) in a team that values software engineering rigor as much as AI innovation.