AI Harness Engineering Product Manager

ATOMMEDIA PTE. LTD.; Singapore; On-site; SGD 150,000 - 210,000; Full time

Job summary

ATOMMEDIA PTE. LTD. in Singapore seeks a technical product-engineering leader who bridges product management and engineering to build the agent harness that sits between AI models and users in our advertising suite.

You will define the architecture, own integrations, and shape how agents perform, reliably and safely. This greenfield role requires hands-on delivery, strong system thinking, and cross‑functional collaboration with researchers and engineers.

Qualifications

• 4+ years of software engineering experience with full-stack or platform focus.
• Hands-on experience building testing frameworks, evaluation systems, or developer tooling.
• Familiarity with LLM-based systems, AI agents, or autonomous workflows.
• Product thinking with ability to define metrics and translate signals into priorities.
• Experience with tool use, reasoning, planning, and context engineering concepts.

Responsibilities

• Design and own the agent harness framework—architecture between model and user.
• Integrate internal systems and manage access permissions for reliable operation.
• Build evaluation pipelines and benchmarks for performance, reliability, and safety.
• Develop feedback loops to convert real-world signals into roadmap decisions.
• Collaborate with ML researchers to co-evolve the harness with the model.
• Diagnose failures, hallucinations, and orchestration issues; contribute to code reviews.

Skills

Software engineering

AI/LLM

CI/CD

Product thinking

System design

UI/UX sensibility

English communication

Tools

LangChain

LangGraph

Kubernetes

CI/CD pipelines

Job description

What you’ll do

This is a new breed of role — sitting at the intersection of product management and agentic engineering. You will define, build, and own the harness layer that makes AI agents actually work in our suite of advertising products. The harness is everything between the model and the user: the infrastructure, connectors, evaluation systems, and feedback loops that determine whether our agents are reliable, useful, and safe. You are not a traditional PM who hands specs to engineers, nor a pure engineer who ships features without a user lens. You are both — with a deep enough technical foundation to get your hands dirty, and a sharp enough product instinct to know what matters. This is a greenfield function. There is no existing playbook, no inherited team structure. The right person sees that as the opportunity, not the risk.

Key responsibilities

01 Harness Design & Ownership

Design and own the agent harness framework — the full layer between model and user, including connectors, execution environments, orchestration scaffolding, and evaluation pipelines. This is a greenfield build; you define the architecture and set the standards.

02 Systems & Connector Integration

Build and maintain integrations to internal systems (e.g. Slack, Hubspot, Google Drive, codebases), managing access permissions and ensuring integrations do not decay over time. Own the reliability of the agent’s ability to act in our environment.

03 Evaluation & Performance Benchmarking

Develop evaluation pipelines and benchmarking systems to measure agent performance, reliability, and safety in both production and simulated environments. Define what “the agent is genuinely helping more people in more scenarios” means — and own the metrics that prove it.

04 Feedback Loops & Signal Extraction

Act as the primary signal source: use internal real-world tasks as training feedback, and engage the user community to extract product signals at scale. Translate raw signal into prioritised harness improvements and roadmap decisions.

05 Research Collaboration & Roadmap

Collaborate closely with model/ML researchers to co-evolve the harness alongside the model — influencing what gets trained, not just what gets shipped. Own the agent harness product roadmap, connecting researchers, engineers, and users around a coherent vision.

06 Diagnostics & Engineering Contribution

Diagnose and troubleshoot agent failures, hallucinations, orchestration errors, and integration issues — with enough technical depth to root‑cause, not just elevate. Contribute to architecture discussions, code reviews, and engineering ceremonies.

What we’re looking for

• 4+ years of software engineering experience— full-stack or platform engineering preferred. ML/LLM model training is not the target profile for this role.
• Hands‑on experience building testing frameworks, evaluation systems, or developer tooling. Strong grasp of CI/CD, source control, and Agile development practices.
• Familiarity with LLM-based systems, AI agents, or autonomous workflows — and genuine working knowledge of:LLM API, KV Cache, Agent Loop, Tool Use, Reasoning, Planning, MCP, Memory, Subagent, Multi-Agent, Prompt Engineering, and Context Engineering.
• Product instinct developed through2+ years of product thinking(formal PM title not required). Demonstrated ability to define metrics, design systematic data collection, and translate user signals into prioritised decisions.
• Deep power user of agent products —Claude Code, Cursor, Manus, Codex, GitHub Copilot, Cowork, or similar. Strong intuition for model behaviour, developer experience, and end‑user experience across all forms of agent product.
• Comfort operating as asole contributor in a greenfield functionwith no existing team or playbook to inherit. Pragmatic builder who ships rather than theorises, with a strong sense of ownership.
• Genuine enthusiasm for agentic AI as the future of how products are built — ambivalence about AI‑assistance is a disqualifier. Personal tinkering or side projects with AI tooling is a strong signal.
• UI/UX design sensibility— ability to produce product prototypes and design briefs with AI assistance. Strong English communication for cross‑functional and external collaboration.

Nice to haves

• Experience with AI agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, or similar).
• Experience with LLM evaluation frameworks and prompt/context testing.
• Knowledge of distributed systems, Kubernetes, cloud infrastructure, or observability tooling for AI systems.
• Experience building simulation environments or synthetic test datasets.
• Open‑source community involvement, or experience leading a product roadmap as the sole PM in a small team.
• Deep collaboration experience with ML researchers.

What we offer

Cutting‑edge technology— Work at the frontier of agentic AI, building systems that don’t yet have a playbook. You won’t be incrementing existing products — you’ll be defining a new category.

Career growth potential— With A65’s expansion plans and eventual IPO, you will grow alongside a company building the future of marketing intelligence — with a seat at the table from day one.