ML Engineer II

• It involves collaborating with engineers to develop software components using modern CI/CD and deployment practices such as blue-green deployments, observability, canary deployments, Kubernetes, feature flags, and automated rollbacks, as well as building and maintaining reporting and dashboards that provide ongoing visibility into GenAI operations.
• The role requires applying sound engineering judgment to resolve technical issues and contributing to generative AI-enabled software architecture and design, with a focus on releasing and triaging deployed software.
• Success is measured by the delivery and effectiveness of generative AI-enabled software solutions, the quality and reliability of the components you own, and your growing technical influence within the team.
• The work impacts the organization by enabling advanced software capabilities that improve operational efficiency and customer experience., * Contribute to the architecture, design, and production deployment of enterprise-scale software releases using generative AI systems, including LLM orchestration, agent/skill creation, retrieval-augmented generation, evaluation, and operational monitoring.
• Design and implement modular AI agents that connect to our core systems, and build clean abstractions that let non-technical users leverage these agents in useful workflows without writing code.
• Build automation that streamlines release operations, operational reporting, and engineering workflows for generative AI-enabled systems.
• Build and deploy containerized services and automation-packaging applications as Docker images, authoring Helm charts, and deploying to Kubernetes-and build and maintain the CI/CD pipelines (GitLab CI) that automate build, test, and release.
• Build and maintain dashboards and reports that provide ongoing visibility into release health, model and system performance, and operational metrics for deployed GenAI solutions.
• Build observability capabilities through dashboards, monitoring, alerting, tracing, and operational telemetry.
• Troubleshoot complex production issues across applications, cloud infrastructure, Kubernetes, APIs, distributed systems, and enterprise integrations.
• Provide technical guidance by mentoring junior engineers and contributing to technology decisions within the team.
• Apply and help improve GenAI engineering standards, reference architectures, and guardrails that ensure scalability, security, cost efficiency, and responsible AI use.
• Contribute reusable templates, patterns, and documentation that accelerate the team’s ability to deliver.
• Develop generative AI-enabled software designs and improvements that enhance existing systems and processes.
• Produce clear technical documentation and architecture descriptions for internal and external stakeholders.
• Collaborate closely with Product Management, Platform Engineering, SRE, Security, UX, and engineering teams to deliver scalable, secure, and reliable software solutions.
• Also responsible for other duties/projects as assigned by business management as needed.

• Bachelor’s Degree plus 4 years of related work experience OR Advanced degree with 2 years of related experience (Required)
• Acceptable areas of study include Computer Science or Engineering (Required)
• 4 7 years of technical engineering experience (Required), * Hands-on experience building and shipping GenAI features in production (LLM orchestration, agent frameworks, RAG) that serve real users reliably.
• Experience applying AI engineering standards, reference architectures, and governance models within a team or product area.
• Demonstrated ability to build composable AI components and internal tooling that enable non-technical users to build workflows without engineering support.
• Experience mentoring engineers and collaborating across functions to deliver shared outcomes.
• Experience with release management and CI/CD deployment best practices.
• Hands-on experience building services and automation scripts, authoring Dockerfiles and Helm charts, deploying to Kubernetes, and building and maintaining CI/CD pipelines (GitLab CI or similar).
• Experience with data warehousing, SQL, and ETL processes, ideally including Snowflake.
• Experience building dashboards and reports with BI and reporting tools such as Power BI, Streamlit, Tableau, or similar.

Knowledge, Skills and Abilities:

• Communication (Required)
• Customer Service (Required)
• Analytics (Required)
• Technical Writing (Required)

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TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions tour clients world-wide. Our client provider of digital technology and transformation, information technology and services