AI Software Engineer

Job : 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 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.

Key Responsibilities

Software Engineering & Cloud Infrastructure (Primary Focus)

Design, build, and optimize
cloud-native backend services
) 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 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
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).

Collaboration & Ownership

Work cross-functionally withproduct, frontend, and data teamsto 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 systemdesign.

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, 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).

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 a related field
.

Preferred Qualifications

Experience with
full-stack development
(frontend frameworks likeReact/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, 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.