Business Support Engineer - BizAI

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 AI-driven solutions and a track record of improving end-to-end support experiences.

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 coaching and mentoring peers on technical troubleshooting and project execution

Minimum Qualifications

3+ 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 outages impacting Meta Partners

Experience with the full web stack, REST API, Python, PHP/Hack, JavaScript/React development along with debugging and bug management support

Knowledge of fine-tuning and optimization of PyTorch models and and experience with at least one LLM such as Llama, GPT, Claude, or Falcon

Experience in communicating with technical and business audiences and writing technical documentation

Experience in assessing, analyzing, and resolving operational issues using data analysis (SQL)

Preferred Qualifications

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Experience in partner-facing or customer-centric engineering roles

Experience transforming data, model selection/training/optimization, and deployment at scale

Experience with Open Source cloud stacks like Kubernetes, Kubeflow, Docker containers

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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

Experience collaborating in cross-cultural engineering environments with international stakeholders

Hands-on experience working with large language models and AI agents

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)