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AI Software Engineer (Generative AI & Cloud-Native Systems) - 3-4 years in software development + 1-2 years in AI (agentic / generative) - Immediate joiners only

AI Software Engineer (Generative AI & Cloud-Native Systems) - 3-4 years in software development + 1-2 years in AI (agentic / generative) - Immediate joiners only

Blue Cloud Softech Solutions LimitedBangalore, Bangalore (division), India
1 day ago
Job description

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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