Applied AI Engineer

Onebyzero; Philippines; Hybrid; PHP 1,200,000 - 1,800,000; Full time

Job summary

OneByZero in Manila/Makati region is seeking an Applied AI Engineer with 3–4 years of experience to design and build production-grade GenAI systems. You will contribute architecture coverage, work on end-to-end LLM system design, RAG pipelines, and multi-agent architectures with a strong focus on production readiness.

You will design, pre-train, fine-tune, and deploy models, optimize for latency and cost, and collaborate with cross-functional teams to deliver high-impact solutions across Asia

Qualifications

• 3–6 years of experience in ML engineering, LLMs, or model development roles.
• Hands-on experience with CPT, SFT, LoRA, or QLoRA on LLMs.
• Strong Python programming skills and production-ready code.
• Experience deploying models to cloud with cost and latency considerations.

Responsibilities

• Design end-to-end LLM system architecture for real-world production use cases.
• Pre-train and fine-tune LLMs for client-specific use cases.
• Design and run evaluation pipelines to benchmark performance and cost.
• Optimize models for latency, throughput and token efficiency in production.
• Integrate fine-tuned models into multi-agent pipelines.
• Implement prompt versioning, rollback strategies and monitoring.
• Translate client requirements into model adaptation strategies with clear success criteria.
• Contribute to knowledge sharing on fine-tuning and tooling.

Skills

Strong problem-solving

Client-facing delivery

Independent work

Strong communication

Education

AWS AI Practitioner Certification

AWS Certified Machine Learning Engineer

AWS Certified Solutions Architect

Anthropic certification

Tools

Python

Docker

Kubernetes

Bedrock

OpenSearch

Lambda

ECS/EKS

Pinecone

Weaviate

Milvus

FAISS

Job description

Makati, Philippines | Posted on 09/28/2026

OneByZero is a Frontier Systems Integrator building agentic AI systems for leading banks, telcos, insurers, and retailers across Asia Pacific. We work on mission-critical problems for organizations operating the region's financial and digital infrastructure. Operating exclusively on AWS, we combine deep technical expertise with strong partnership programs that enable innovation, scale, and delivery excellence. We build with people who are technically serious, outcome-focused, and passionate about solving complex enterprise challenges.

The Role

We are seeking a Applied AI Engineerwith 3–4 years of experience to help design and build production-grade GenAI systems. In this role, you will contribute architecture coverage across the team—reviewing system designs, identifying gaps, and guiding technical decisions at the solution level. You will work on end-to-end LLM system design, Retrieval-Augmented Generation (RAG) pipelines, and multi-agent architectures, with a strong focus on production readiness. Strong coding depth is non-negotiable.

What You Will Do

• Design and contribute to end-to-end LLM system architecture for real-world enterprise use cases (from requirements to production).
  
• Pre-train, fine-tune LLMs and domain-specific models using techniques such as CPT, SFT, LoRA, and QLoRA for client-specific use cases.
  
• Design and run model evaluation pipelines to benchmark performance, accuracy,and cost across different fine-tuning approaches.
  
• Optimise models for latency, throughput, token efficiency, and inference cost in production environments.
  
• Work alongside agent orchestration and architecture teams to integrate fine-tuned models into multi-agent pipelines.
  
• Implement prompt versioning, rollback strategies, and model monitoring to ensure reliability post-deployment.
  
• Translate business requirements from client engagements into model adaptation strategies with clear success criteria.
  
• Contribute to internal knowledge sharing on fine-tuning best practices, tooling, and emerging techniques.
  
• Define enterprise integration patterns for GenAI systems (identity/access controls, auditability, data boundaries, governance, and compliance alignment).
  
• Improve production reliability: latency/throughput optimization, token efficiency, cost control, and robust failure handling.
  
• Collaborate with cross-functional stakeholders (engineering, data, product, client teams) to deliver high-impact solutions on tight timelines.
  
• Contribute hands-on code, perform code reviews, and raise the engineering bar through strong software fundamentals.
  

Requirements

What We Are Looking For

• 3–6 years of experience in ML engineering, LLMs, or model development roles.
  
• Hands-on experience with Continual pre-training (CPT), supervised fine-tuning (SFT), LoRA, or QLoRA on LLMs.
  
• Strong Python programming skills. Ability to write clean, testable, production-ready code.
  
• Experience running model evaluation and benchmarking pipelines in a structured way.
  
• Solid understanding of transformer architectures and how fine-tuning affects model behaviour.
  
• Experience deploying fine-tuned and pre-trained models to cloud environments with attention to cost and latency.
  
• Strong problem-solving skills with the ability to work independently on client-facing projects.
  
• Solid software engineering fundamentals: APIs, data structures, testing, debugging, and performance optimization.
  
• Ability to review designs, communicate trade-offs clearly, and collaborate effectively in a fast-paced environment.
  
• Experience with AWS-native GenAI building blocks (e.g., Bedrock, OpenSearch, Lambda, ECS/EKS) and secure enterprise deployments.
  
• Experience with vector databases/search engines (OpenSearch, Pinecone, Weaviate, Milvus, FAISS) and retrieval optimization.
  
• Experience with containerization and orchestration (Docker, Kubernetes).
  
• Experience building evaluation/observability pipelines for LLM systems and implementing safety/guardrail patterns.
  
• Consulting or client-facing delivery experience.
  

What Success Looks Like

• The client has a production agentic system that is performing, that their team understands and can operate, and that is producing the outcome that justified the investment.
  
• OBZ has something reusable from every engagement you touch: a design pattern, an evaluation framework, a deployment runbook, a code module that the next squad picks up without starting from scratch. Your work compounds across the firm, not just within a single account.
  
• You are a more capable engineer at the end of every engagement than you were at the start. You have pushed into something you had not done before, solved a problem that was not in the playbook, and left evidence of that growth in the IP you contributed and in the engineers you developed alongside you.
  
• When all three of these are consistently true, you are succeeding at this role.
  

Certifications

• AWS AI Practitioner, AWS Certified Machine Learning Engineer Associate, AWS Certified Solutions Architect (Associate or Professional), and Anthropic certification are all relevant and valued. Certification expectations are calibrated to level and assessed during the hiring process.

What We Offer

Competitive compensation benchmarked to market. The opportunity to work on AI transformation programmes that matter to the institutions millions of people in Asia Pacific depend on. A team that spans 10 markets, brings together some of the most experienced AI, data, and enterprise technology practitioners in the region, and moves fast without losing quality. Access to the AWS partnership programmes and frontier AI tooling that most firms in this region do not have. A firm that is building something genuinely new, with a leadership team that has scaled and delivered before.

OneByZero is an equal opportunity employer. We are committed to building a diverse and inclusive team and welcome applications from candidates of all backgrounds.

Why OneByZero

Agentic AI in regulated enterprise is the hardest version of this problem. The clients are the institutions that run the financial infrastructure of Asia Pacific. The constraints are real. The stakes are high.

OBZ is one of the few firms in the region doing this work at production scale in regulated industries, with an AWS partnership that puts you at the frontier of what is deployable. You will build systems that matter, work in squads small enough that your contribution is visible, and use AI coworkers as a genuine force multiplier.

If you are at the start of your career and want to be shaped by the hardest agentic engineering problems in the region, this is where that happens. If you are experienced and want to work on problems that are still genuinely unsolved, this is also where that happens.