AI Developer II

Req number:

Employment type:

Worksite flexibility:

Who we are

CAI is a global services firm with over 9000 associates worldwide and a yearly revenue of $1.3 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 rightwhatever 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

Job Description

We are seeking an AI Solution Architect who can Design and deliver the end-to-end AI solution architecture. This position will be Full-time and Hybrid position.

What Youll Do

End-to-End Solution Architecture

• Select solution patterns: classical ML vs LLM vs hybrid retrieval-augmented generation (RAG) orchestration AI agents (if appropriate).

• Define system architecture: APIs services event flows data interfaces identity and access.

• Define non-functional requirements: latency availability scalability resilience.

Model Behavior Quality & Evaluation (explicit ownership)

• Own model/prompt behavior and quality: defineevaluation strategyand acceptance thresholds.

• Implement evaluation: offline tests golden datasets regression testing for prompts/models.

• Own error analysis and failure mode mitigation (hallucinations bias data leakage prompt injection risks).

• Define safe completion behavior for GenAI: guardrails filters and escalation paths.

Integration & Deployment Ownership

• Build/oversee integration into TEs applications and business workflows.

• Own the deployment approach (CI/CD environment configuration secrets feature flags).

• Ensure observability: logging metrics tracing model/prompt versioning.

Security Privacy Compliance-by-Design (technical)

• Ensure proper data handling encryption access controls and least privilege.

• Ensure compliance requirements translate into technical controls (e.g. retention PII handling).

Key Deliverables

• Solution architecture (HLD/LLD) interface contracts integration plan

• Model/prompt strategy and evaluation plan (metrics thresholds test suites)

• MVP/pilot reference implementation production deployment design

• Monitoring dashboards: latency cost errors quality signals

• Runbooks: incident response rollback plan version management

• Security & privacy technical design notes (controls data paths)

Decision Rights (owns the call)

• Technical architecture and build-vs-buy decisions (within agreed governance and project-lead)

• Model/prompt selection and evaluation gates for release readiness

• Integration approach and production readiness from a technical standpoint

Key Interfaces

• Collaborate with application owners / platform teams (cloud DevOps endpoint teams)

• Security engineering IAM network teams

• Data role training data quality lineage and governance

• Business SMEs for evaluation scenarios and acceptance tests

What Youll Need

Required

Bachelor degree: Computer Science Software Engineering Data Science Artificial Intelligence / Machine Learning Applied Mathematics or Engineering (with strong CS content)

QUALIFICATIONS & EXPERIENCE

Artificial Intelligence & Machine Learning

Model evaluation techniques (precision/recall ROC grounding metrics)

Overfitting generalization bias and robustness

Understands LLM concepts:

• Prompt engineering

• Finetuning vs promptonly approaches

• Failure modes (hallucination prompt injection)

Software Architecture & Engineering

• Distributed systems concepts

• API design and integration patterns

• Microservices eventdriven architectures

• Observability (logging metrics tracing

3 years of overall experience

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 or (888).