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About the Role
We are looking for an engineer to play a key role in providing technical, engineering support to Meta’s Advertising partners, Customers and clients globally. You will have the opportunity to work together with a global team of Business Support Engineers who are expert in Meta’s Adtech to provide proactive and reactive support for partner issues and integrations while partnering with our broad cross-functional teams to ensure a high quality for our products and satisfactory experience for our partners and customers. We work directly with Platform and Infrastructure teams to investigate and resolve issues reported by our partners, to properly assess and agree on actions to be taken for necessary fixes and continuous improvements in our products and deployments.
As a Business Support Engineer working on Ads at Meta, you will primarily support on Meta’s Ads Products but also have the opportunity to work cross-functionally in several of our key business areas across Business Messaging, Telecommunications and apply your technical experience and customer service skills with a product-focused mindset to ensure that key insights are communicated to our product teams. We are looking for people who have subject-matter knowledge in managing and maintaining integrations with third-party services, who help foster developer/business relationships, who demonstrate critical thinking and blending systems design with business needs, and who have a desire to improve the support experience of our customers. As a Business Support Engineer, you will understand industry trends, partner solutions and integrations, and their implications to our product roadmap. You will work closely with other regional offices and partnership teams to support a broad range of partners across the globe to integrate Meta’s Business Products into their offering.
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Responsibilities
- Architect global troubleshooting methodologies and escalation frameworks to enhance the team's operational excellence and engineering standards
- Co-Lead the team's AI-native technical strategy, driving large-scale adoption of AI tools to transform support workflows and value delivery
- Build, launch, and optimize complex AI architectures using Llama and other LLMs, owning the full lifecycle from prototype to production
- Develop performance monitoring systems for partner integrations to ensure high availability; leverage metrics to proactively identify issues and drive improvements across teams
- Provide 24/7 on-call support coverage via rotation schedule (including weekends)
- Partner with internal and cross-functional leadership to identify systemic support issues and influence roadmaps with data-driven proposals to eliminate root causes
- Set technical communication standards, authoring reference architectures, and representing the team internally and externally
- Manage complex, cross-functional programs while fostering continuous AI/ML learning within the team
- Lead zero-to-one initiatives, anticipating future platform and partner needs
Minimum Qualifications
- 8+ Years of experience as Software engineering or Site Reliability Engineering background
- Proven experience in API development on cloud-based infrastructures, being able to debug, identify root causes and resolve independently any outages that are impacting Meta Partners
- Experience with the full web stack, REST APIs, Python, PHP, and JavaScript/React development, along with debugging and bug management
- Knowledge of fine-tuning and optimizing PyTorch models and experience with at least one LLM, such as LLaMA, GPT, Claude, or Falcon
- Experience communicating with technical and business audiences and writing technical documentation
- Experience in assessing, analyzing, and resolving operational issues using data analysis (SQL)
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Preferred Qualifications
- Experience transforming data, model selection/training/optimization, and deployment at scale
- Experience in partner-facing or customer-centric engineering roles
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Experience with Open Source cloud stacks like Kubernetes, Kubeflow, Docker containers
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Hands-on experience working with large language models and AI agents
- Experience collaborating in cross-cultural engineering environments with international stakeholders
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Experience building cloud solutions on any cloud
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