Senior AI Delivery Engineer – Full Stack & GenAIOps (35694)

Senior AI Delivery Engineer – Full Stack & GenAIOps Contract: 6 Months + Extension Location: 100% Remote – North or South America Hours: 37.5 Hours per Week Start: Early September Position Overview We are seeking a Senior AI Delivery Engineer to help turn technically capable AI services into production-ready, end-to-end solutions that deliver measurable value to users. This is a hands-on engineering and delivery role for someone who can work directly with business stakeholders, operational users, domain experts, data teams, and technical teams to understand complex workflows and translate them into working AI-enabled applications, integrations, and production solutions. The successful candidate will operate across the full solution lifecycle — from technical discovery and solution definition through hands-on development, integration, testing, deployment, production validation, and early-life support. The role requires a strong combination of AI engineering, full-stack/integration development, cloud technologies, APIs, DevOps/GenAIOps, CI/CD, observability, and production support. Python is expected to be part of the engineering toolkit, while broader technology and cloud experience is important. Candidates should be comfortable working across platforms rather than being dependent on a single technology stack. This is fundamentally an engineering role, not a Business Analyst, Project Manager, implementation coordinator, or pre-sales position. Key Responsibilities AI Solution Delivery & Technical Discovery Work directly with business teams, operational users, domain experts, data owners, and technical stakeholders to understand existing processes and identify where AI-enabled solutions can create measurable value. Analyze workflows, business decisions, rules, handoffs, exceptions, systems, data sources, controls, and success measures. Translate ambiguous business and technical requirements into clear technical scope, executable work packages, thin vertical slices, and end-to-end acceptance criteria. Bridge the gap between AI services/models and the applications, systems, workflows, and users that consume them. Support technical discovery, solution design, UAT, production readiness, adoption, and handover. Full-Stack Engineering & Integration Personally design, build, integrate, test, and support the components required to deliver end-to-end AI solutions. Develop user-facing applications and operational tools such as: Reviewer workbenches and evidence views Work queues and workflow interfaces Approval and correction capabilities Human-in-the-loop interactions Build and maintain backend services, APIs, and backend-for-frontend components. Develop adapters, data transformations, and integrations connecting AI services with enterprise applications, workflows, and source-data systems. Implement authentication, role-based authorization, session/state management, audit history, trace identifiers, and safe-action confirmation. Develop integration, API, and contract tests across service boundaries. Ensure solutions are reliable, maintainable, secure, and appropriate for enterprise production environments. GenAIOps, DevOps & Production Engineering Own the operationalization of AI-enabled solutions from development through production. Build and maintain CI/CD pipelines, reusable project structures, and Infrastructure as Code (IaC). Manage environment configuration, artifact/configuration versioning, deployment promotion, approvals, rollback, and release traceability. Implement appropriate automated evaluation and release gates for AI/GenAI solutions. Incorporate security and dependency scanning into engineering and deployment workflows. Manage secrets, workload identity, and secure configuration practices. Establish production observability through logs, traces, dashboards, telemetry, and alerts. Implement mechanisms for user and operational feedback capture. Create runbooks covering incident diagnostics, recovery procedures, production support, and operational handover. Production Ownership & Continuous Improvement Carry contributed code and configuration through build, testing, deployment, production validation, and early-life support. Troubleshoot production and integration failures directly using code, logs, traces, and telemetry. Diagnose issues across application, integration, AI service, cloud, and deployment boundaries. Convert recurring integration, deployment, and production-support requirements into reusable templates, tooling, and platform automation. Continuously improve engineering practices to make AI solutions easier to deploy, operate, monitor, and support. Required Experience & Skills Senior-level experience delivering production software, AI-enabled applications, or enterprise technology solutions. Strong hands-on software engineering experience, with Python highly relevant to the environment. Demonstrated experience taking solutions through the complete engineering lifecycle from requirements/discovery through production deployment and support. Experience developing and integrating APIs, backend services, enterprise applications, and data-driven systems. Ability to work across the full solution stack, including user-facing components, backend services, integrations, security, deployment, and production operations. Strong understanding of cloud-based architectures and services; experience across Azure and/or other major cloud platforms is valuable. Experience with CI/CD, Infrastructure as Code, automated testing, deployment automation, and production release practices. Experience implementing logging, monitoring, tracing, telemetry, dashboards, and operational alerting. Understanding of enterprise security concepts including authentication, authorization/RBAC, secrets management, workload identity, and auditability. Strong troubleshooting skills with the ability to investigate issues directly through code and production telemetry. Strong communication skills and demonstrated ability to work directly with clients, business stakeholders, technical teams, and operational users. Comfortable operating in ambiguous environments and translating business problems into practical, executable technical solutions. Highly Valued Experience Experience delivering Generative AI / GenAI applications into production. Experience with GenAIOps, LLMOps, MLOps, or AI application operationalization. Experience building human-in-the-loop AI workflows, reviewer applications, approval processes, or operational AI workbenches. Experience establishing automated AI evaluation, quality, or release-gating processes. Experience within financial services, banking, credit unions, or other regulated enterprise environments. Microsoft Azure experience is an asset, although the role is not intended to be limited to a single cloud or technology platform. What We're Looking For This role is best suited to a builder who can deliver, not someone who operates solely at the strategy or coordination level. The ideal candidate can sit with users and stakeholders to understand a difficult operational problem, translate it into a technical solution, write and integrate the code, establish the deployment and operational framework, take the solution into production, and troubleshoot it when something goes wrong. You should be equally comfortable discussing requirements with a business stakeholder and working directly with APIs, application code, integrations, cloud environments, CI/CD pipelines, logs, traces, and production systems. ,

The role involves translating complex business workflows into production-ready AI applications and end-to-end solutions. Responsibilities include designing full-stack components, managing GenAIOps pipelines, and providing production support and observability.

Requires senior-level experience in software engineering and delivering AI-enabled enterprise solutions, with strong proficiency in Python and cloud platforms. Candidates must be capable of handling the full lifecycle from technical discovery to production deployment and troubleshooting.

• Microsoft AzureSouhaitée
• CI/CDSouhaitée
• PythonSouhaitée

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Python
• GenAIOps
• Full-Stack Development
• CI/CD
• Azure
• API Integration
• Infrastructure as Code
• LLMOps
• MLOps
• DevOps
• Cloud Architecture
• Production Engineering
• Observability
• Backend Development
• Human-in-the-loop AI
• Technical Discovery

• Software
• Technology
• Engineering
• Data & Analytics
• Consulting

Expérience minimale

5+ ans

Langue de l’offre

anglais

Heures de travail

38 heures par semaine

Niveau d’expérience

Mid-Senior level