Job Description

Req number:

R6259

Employment type:

Full time

Worksite flexibility:

Hybrid 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 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.





We are looking for an AI Software Developer to design, build, and maintain cloud-native backend services, infrastructure, and APIs that power AI features. This position will be full-time and hybrid.



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.