AI Software Developer

Req number:

Employment type:

Worksite flexibility:

Who we are

CAI is a global technology services firm with over 8,500 associates worldwide and a yearly revenue of $1 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.

Job Summary

AI Software Developer

What You’ll Do

• 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.

What You'll Need

Required:

• 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.

SPACE

Preferred:

• 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.

Physical Demands

• Ability to safely and successfully perform the essential job functions

• Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc.

• Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor

Reasonable accommodation statement

If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to application.accommodations@cai.io or (888) 824 – 8111.