Meta recently launched its Business Agent, helping businesses of every size use AI to boost productivity and deliver more personalized customer experiences. Business Support Engineering will be at the forefront of this shift, and we’re looking for an engineer to play a pivotal role supporting Meta’s partners bringing demonstrated experience in distributed systems and API troubleshooting and a focus on improving the end-to-end support experience. As a Business Support Engineer, you will work closely with cross-functional teams and business partners across the globe, incorporating AI-driven business solutions into their service offerings. You will track industry advancements and partner experiences, evaluating their impact and influencing the product's strategic roadmap.
Responsibilities
Provide proactive and reactive engineering support for partners, independently managing complex outages to ensure high partner satisfaction Troubleshoot large-scale distributed systems and partner integrations, championing operational excellence and engineering craftsmanship Leverage AI tools to accelerate troubleshooting, automate repetitive tasks, and scale your impact with an 'AI native' mindset Build, launch, and optimize AI solutions 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 oncall support coverage via rotation schedule (including weekends) Collaborate with Platform and Infrastructure teams to investigate issues, align on fixes, and drive continuous product improvement Create clear documentation, specs, guides, and presentations to communicate complex AI concepts to diverse audiences, scaling the team's knowledge internally and externally Drive end-to-end execution, using sound judgment to manage stakeholder expectations and ensuring clear alignment. Develop and share AI/ML expertise, actively coach and mentor peers on technical troubleshooting and project execution
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 5+ years of experience in Software Engineering or Site Reliability Engineering Proven experience in API development on cloud-based infrastructures, with the ability to debug, identify root causes, and independently resolve outages impacting Meta partners Experience with the full web stack, REST APIs, Python, PHP/Hack, and JavaScript/React development, along with debugging and bug management Knowledge on fine-tuning and optimizations of PyTorch models and with at least one LLM such as LLaMA, GPT, Claude, Falcon, etc Experience in communicating with technical and business audiences and writing technical documentation Experience in assessing, analyzing, and resolving operational issues using data analysis (SQL) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience working in engineering environments with geographically distributed, cross-cultural teams and international stakeholders Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience in partner-facing or customer-centric engineering roles Hands-on experience working with large language models and AI agents Experience transforming data, model selection/training/optimization, and deployment at scale Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience with Open Source cloud stacks like Kubernetes, Kubeflow, Docker containers Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience building and deploying solutions on cloud platforms (e.g., AWS, GCP, Azure) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)