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Own and evolve Mitratech’s AWS DevOps and platform engineering practice, including multi-platform CI/CD, Terraform and AWS CDK infrastructure as code, AWS compute, networking, databases, security controls, and AI/analytics operations. Design reusable pipelines, enforce quality and security gates, manage multi-account environments, improve automation reliability, mentor engineers, and participate in on-call support.

Owns and administers Snowflake data platforms, including account governance, performance and cost optimization, security, access management, and reliability. Maintains Fivetran ingestion connectors and dbt pipelines, integrates monitoring and alerting, troubleshoots data issues, and supports cloud DevOps across AWS, Azure, and OCI. Partners with engineering, security, and compliance teams on access reviews, audit evidence, incident response, and platform improvements.

Lead end-to-end AI product work across Mitratech's shared AI platform and customer experiences. Set strategy and roadmap, convert ambiguous problems into shipped, enterprise-grade features, partner with engineering and design, prioritize based on customer feedback, and represent products to senior stakeholders and customers.

Leads product program operations across a multi-product GRC SaaS portfolio. Establishes operating cadences, planning and reporting rhythms, launch and release readiness, cross-functional dependencies, milestone tracking, prioritization, and decision governance. Connects Product with Product Marketing, Sales, Customer Success, Engineering, Enablement, and Support to ensure coordinated delivery and go-to-market execution. Builds scalable playbooks, tooling, dashboards, and accountability systems while facilitating portfolio trade-offs and maintaining commitment integrity.

Provides technical leadership and architectural oversight for scalable AI-integrated software. Designs agentic AI systems, MCPs, APIs, RAG solutions, microservices, and prompt strategies; decomposes product goals into engineering tasks; reviews human- and AI-generated code; mentors distributed developers; and improves DevOps and CI/CD pipelines with AI-enhanced testing and reviews.

Develop and execute manual and automated tests for SaaS products, including regression, integration, performance, load, smoke, and user acceptance testing. Create test plans and automation scripts, track and retest defects, validate releases, document results, and collaborate with developers to resolve issues. The role also improves QA methodologies, maintains testing tools, and mentors junior QA team members.

Supports technical escalations by investigating, reproducing, and diagnosing customer issues; communicates with stakeholders; develops and deploys code fixes; performs root-cause analysis; improves documentation and runbooks; and follows secure development practices. The role uses Go and Ruby on Rails, relational databases, SQL, Unix shell scripting, and AI-assisted development tools in a fully remote environment.

Develop and maintain web and AI-based enterprise applications through design, coding, testing, debugging, documentation, and code reviews. Integrate AI-assisted and agentic development tools into engineering workflows, manage context and harness engineering, and mentor teammates on responsible AI use. Collaborate across teams, lead projects and design efforts, improve processes and technology choices, and build secure, scalable services using modern backend stacks, REST APIs, databases, cloud infrastructure, and CI practices.

Coordinate occupational health drug-screening workflows, including confirming and expiring tests, monitoring service status, contacting vendors, locating collection sites, registering applicants, sending test instructions, and ensuring client requirements are met. The role requires confidentiality, organization, production planning, time management, strong written and verbal communication, adaptability, and composure under pressure.

Design, build, and operate production-grade generative AI systems: multi-agent orchestration, end-to-end RAG pipelines, LLM evaluation and integration, scalable AWS-based AI infrastructure, CI/CD for models, observability, data validation, and evaluation frameworks to ensure quality, attribution, and resilience.