Forward Deployed Engineer, APJ

About the role

This position is responsible for end to end engagement quality for AI systems in production. You will drive outcomes for enterprise teams using Arize AX and Phoenix. Your work shapes how customers monitor, test, and ship AI responsibly. The role is client facing and involves hands on technical work across the APJ region. You will partner closely with customers to define their observability strategy and ensure that their AI systems meet stringent reliability and compliance expectations. This role requires a proactive mindset to identify risks early and translate complex technical constraints into clear, actionable plans for stakeholders at all levels. You will act as a technical trusted advisor, guiding clients through the full lifecycle of deploying AI systems in demanding environments.

Key facts

Location is remote based in Singapore. Engagement type is client facing, hands on technical work. Base compensation ranges from 140,000 USD to 170,000 USD annually.

What you'll do

• Architect observability workflows that convert vague requirements into concrete telemetry, ensuring that critical signals are captured from day one.
• Champion evaluation strategies that align model behavior with demanding enterprise standards, defining metrics and thresholds that matter to the business.
• Orchestrate integrations that stitch platform capabilities to messy real world data landscapes, enabling reliable pipelines across diverse tooling.
• Steer multiple concurrent missions while balancing scope, risk, and delivery clarity for clients, maintaining momentum without sacrificing quality.
• Translate executive intent into engineering tasks while safeguarding long term platform coherence and ensuring that solutions remain scalable and maintainable.
• Forge playbooks and patterns that empower both customers and internal specialists effectively, creating reusable approaches that accelerate future engagements.
• Diagnose production issues alongside client teams using Arize AX and Phoenix, isolating root causes and guiding remediation in real time.
• Collaborate with product and engineering teams to surface insights from deployed systems, turning observed failures and edge cases into product improvements.
• Design and implement monitoring dashboards that provide clear, actionable views of model performance, data drift, and system health for non technical audiences.
• Conduct workshops and discovery sessions to clarify objectives, constraints, and success criteria before implementation work begins.
• Guide configuration of alerting and notification policies so that clients can respond quickly to anomalies without being overwhelmed by noise.
• Document integration steps, data schemas, and runtime expectations to ensure smooth handoffs and continuity across engagements.
• Validate that deployed models meet functional and non functional requirements through rigorous testing and observation in staging and production.
• Support knowledge transfer sessions that leave client teams confident and capable of managing their AI observability stack independently.

Requirements

You must have two to five years of experience writing software that powers production AI systems. Experience with Python, Java, or TypeScript is mandatory across at least two of these languages. You need hands on experience with MLOps pipelines and generative AI deployment in live production contexts. Working with AWS, GCP, or Azure plus containers and orchestration is non negotiable. Strong communication that bridges technical depth and executive business outcomes is essential. You manage several client initiatives concurrently while meeting strict timelines without compromise. You are comfortable operating in fast moving environments with evolving priorities and diverse stacks. Genuine enthusiasm for AI observability and evaluation defines your professional drive. You understand the importance of security, privacy, and compliance considerations when working with customer data in production environments. You are willing to work evenings or weekends on occasion to support critical deployments across different time zones.

Nice to have

Experience guiding implementation strategy while influencing product direction in practice is valued.

Skills & tools

Core tools and technologies include Arize AX, Phoenix, Pydantic, Docker, Kubernetes, Git, REST, JSON, Protobuf, and Parquet.

Practical notes

Please note that this role is based in the Singapore area and requires proximity to regional clients in the APJ region. While the company is remote first, the nature of this role requires occasional travel within the region as needed for on site engagements. There may be short notice travel requirements to support critical deployments or customer visits. This role is classified as a contractor or equivalent engagement type as defined by local regulations. You must be eligible to work in the country of assignment without requiring additional visa sponsorship from the company. Applications will be reviewed on a rolling basis, and early candidates are encouraged to apply promptly to secure consideration.