Senior AI Engineers

Job Summary

The Senior AI Implementation Engineer is responsible for owning application delivery end to end in an AI-delegated development model. This role is accountable for ensuring that software solutions are correctly implemented, operationally ready, and aligned to business intent across application code, infrastructure, CI/CD, and observability. The Senior AI Implementation Engineer directs AI coding agents to generate and evolve technical solutions while critically evaluating their output for correctness, completeness, maintainability, architectural alignment, and production fitness.

This role is not centered on manual code production. Instead, it is centered on engineering judgment. The Senior AI Implementation Engineer translates business and technical requirements into precise implementation instructions, defines constraints and validation mechanisms, identifies specification gaps, and ensures that generated output honors domain rules and operational expectations. This role develops solutions that are robust, scalable, observable, and highly operable while driving continuous improvement in AI-assisted delivery practices.

Job Functions

Direct AI agents to generate back end APIs and client applications from detailed specifications and ensure the resulting solutions align to architectural standards, non-functional requirements, and business rules. 35%

Direct AI agents to generate and evolve Terraform infrastructure, CI/CD pipelines, automated test assets, and observability configurations. Review and validate generated output for security, operability, maintainability, release safety, and alignment to production requirements. Ensure effective rollback controls, deployment guardrails, structured logging, distributed tracing, dashboards, alerts, and service-level objectives are in place. 25%

Evaluate and challenge AI-generated implementations to detect incorrect logic, problematic patterns, unnecessary complexity, weak deployment controls, poor operational visibility, and architectural drift. Define validation approaches, including automated tests. Identify gaps in requirements or documentation and contribute corrections to ensure quality and long-term system coherence. 35%

Education:

Level	Degree	Required / Preferred
Bachelor's degree	Computer Science, Information Technology, Engineering, or related field, or equivalent work experience	Required
Master's degree	Computer Science, Information Technology, Engineering, or related field	Preferred

10+ years Professional software engineering experience across application design, implementation, delivery, and support. REQUIRED



10+ years Experience reviewing and validating code and technical artifacts against complex business rules and operational requirements. REQUIRED



7+ years Experience with modern application development using C#/.NET and related backend technologies. REQUIRED



5+ years Experience with React or similar modern frontend frameworks for workflow-driven or administrative applications. REQUIRED



5+ years Experience with infrastructure as code, including Terraform module design, state management, and drift control. REQUIRED



5+ years Experience designing and maintaining CI/CD pipelines in platforms such as Azure DevOps, GitHub Actions, or similar. REQUIRED



5+ years Experience with observability practices including structured logging, metrics, tracing, dashboards, alerts, and SLOs. REQUIRED



5+ years Experience working with relational and NoSQL data technologies, including SQL Server or PostgreSQL and modern NoSQL patterns for scalable application design. REQUIRED



3+ years Experience with containerized deployment models, including Docker, Kubernetes, and Helm. REQUIRED



3+ years Experience with event-driven architectures, message buses, and saga or state machine patterns. PREFERRED



3+ years Experience using Redis for more than basic caching, including atomic operations, distributed locking, counters, or coordination patterns. PREFERRED



3+ years Experience with legacy modernization or translating legacy business behavior into modern implementations. PREFERRED

Knowledge, Skills, Abilities

Strong ability to direct AI agents using precise specifications, bounded tasks, technical constraints, and validation criteria.
Strong ability to evaluate generated code and technical artifacts for correctness, completeness, maintainability, and fitness for production use.
Excellent understanding of software architecture, modular design, dependency control, integration patterns, and distributed systems fundamentals.
Strong understanding of CI/CD, infrastructure as code, release safety, rollback strategies, and production readiness practices.
Strong knowledge of observability, including structured logging, OpenTelemetry concepts, distributed tracing, dashboards, alerts, and SLOs.
Strong understanding of relational and NoSQL data modeling patterns, including when each is appropriate in modern application architectures.
Practical knowledge of Redis beyond simple caching, including atomic operations, distributed locking, counter-style use cases, and coordination patterns.
Ability to detect problematic patterns in AI-generated output, including architectural drift, hidden coupling, weak failure handling, and unnecessary complexity.
Strong understanding of automated testing and verification approaches across unit, API, integration, component, end-to-end, infrastructure, and runtime validation layers.
Ability to reason effectively about translated or reverse-engineered legacy business rules, even when the original implementation language is unfamiliar.
Strong analytical, problem-solving, and risk-based decision-making skills.
Ability to identify ambiguity in requirements and contribute corrections back into documentation and specifications.
Ability to collaborate effectively with architecture, QE, SRE, product, and business stakeholders.
Strong written and verbal communication skills, including the ability to turn ambiguous goals into precise technical direction.
Ability to manage priorities, handle complexity, and maintain sound engineering judgment in a high-throughput AI-assisted environment.
Ability to learn new tools, technologies, and agent-driven workflows quickly.

Licenses/Certifications

AWS Certified Solutions Architect Professional PREFERRED
AWS Certified DevOps Engineer Professional PREFERRED
HashiCorp Terraform Associate PREFERRED
Microsoft Certified: Azure Developer Associate PREFERRED
Microsoft Certified: Azure DevOps Engineer Expert PREFERRED
Certified Kubernetes Application Developer (CKAD) PREFERRED