AI Software Developer **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. **Job Description** 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.