Senior AI Engineers

Akaasa Technologies; United States; On-site; USD 150,000 - 190,000; Full time

Job summary

Akaasa Technologies seeks a Senior AI Implementation Engineer to own end-to-end AI-assisted delivery, translating requirements into precise instructions and ensuring robust, observable, and operable solutions.

The role guides AI agents to build APIs, infrastructure, and tests while upholding architectural standards and production readiness. Strong judgment and domain rule adherence are essential.

Qualifications

• 10+ years professional software engineering across design, implementation, delivery and support.
• 10+ years reviewing and validating code and artifacts against complex business rules.
• 7+ years with C#/.NET and related backend technologies.
• 5+ years with React or similar frontend frameworks.
• 5+ years Infrastructure as Code, Terraform module design and drift control.
• 5+ years CI/CD pipelines on Azure DevOps, GitHub Actions, or similar.
• 5+ years observability including logging, metrics, tracing, dashboards, alerts, and SLOs.
• 5+ years working with relational and NoSQL data technologies (SQL Server, PostgreSQL, NoSQL patterns).
• 3+ years containerized deployment: Docker, Kubernetes, Helm.
• 3+ years event-driven architectures, message buses, saga/state machines.

Responsibilities

• Direct AI agents to generate backend APIs and client apps from detailed specifications and ensure alignment to standards.
• Direct AI agents to generate and evolve Terraform, CI/CD, tests, and observability configurations with security and operability in mind.
• Evaluate AI outputs for correctness, detect architectural drift, and define validation approaches with automated tests.

Skills

Software engineering

Code review

C#/.NET

React

Terraform

CI/CD

Observability

Databases

Docker

Kubernetes

Helm

Event-driven

Message buses

State machines

Redis

Legacy modernization

Education

Bachelor's degree

Master's degree

Tools

SQL Server

PostgreSQL

Job description

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

Experience:

• 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