Artificial Intelligence Engineer

Sierra Ventures; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Sierra Ventures is seeking an experienced ML/AI Engineer to build agent systems and open-weight models for on-premise deployments. You will design tools, orchestrate models, and own context engineering from prompts to retrieval.

You will fine-tune open-weight models, deploy with Docker, and evaluate reliability across evolving agent and model versions, delivering robust, production-ready solutions.

Qualifications

• 2–5 years of production ML or LLM system experience.
• Strong Python, with solid engineering practices (testing, Git, APIs).
• Hands-on agent development: tool calls, structured outputs, prompts, context.
• Hands-on fine-tuning of open-weight models (PyTorch, HF libs).
• Experience deploying models with Docker and GPU inference.
• Experience designing objective evaluations for models/agents.

Responsibilities

• Design and build agent tools and skills for planning scans and generating robot programs.
• Orchestrate multi-model routing to balance accuracy, latency, and cost.
• Own context engineering: prompts, tool schemas, and retrieval over knowledge.
• Develop deterministic evaluation harnesses and benchmarks.
• Versioning experiments and release workflows; build logs for debugging.

Skills

Python

ML system design

Fine-tuning models

Docker

Agent development

Education

CS degree (optional)

Tools

PyTorch

Hugging Face

vLLM

Job description

About your role

You'll build the agent and the models behind it, from tool and skill design to fine-tuning and serving open-weight models on customer premises.

Agent engineering

• Design and build agent tools and process skills that let the agent plan scans, generate robot programs and resolve motion errors.
• Build multi-model orchestration, routing tasks across large and small models to balance accuracy, latency and token cost.
• Own context engineering: prompts, tool schemas, and retrieval over process knowledge.

Models

• Fine-tune open-weight large language models and vision-language models (supervised fine-tuning, Low-Rank Adaptation and its quantised variant) on domain data.
• Distill large teacher models into small, deployable models.
• Serve and optimise models on graphics processing units for on-premises and airgapped deployments (quantisation, batching, vLLM or similar).

Evaluation and reliability

• Build deterministic evaluation harnesses and benchmark suites; track regressions across model and agent versions.
• Own model and dataset versioning, experiment tracking and release workflows.
• Build logging and tracing for agent runs to support debugging and reliability.
• Curate training and evaluation datasets for agent and model development.

Requirements

• 2–5 years building machine learning or large language model systems that reached production.
• Strong Python and solid engineering practice (testing, Git, code review, application programming interfaces).
• Hands-on agent development: tool and function calling, structured outputs, prompt and context engineering.
• Hands-on fine-tuning of open-weight models (PyTorch, Hugging Face, parameter-efficient fine-tuning libraries).
• Experience designing evaluations that measure model or agent quality objectively.
• Experience deploying models (Docker, graphics processing unit inference, cloud platforms).

Good to have

• Multi-agent or subagent architectures.
• Distillation, quantisation, or on-premises and airgapped deployment.
• Vision-language models, 3D point clouds, or computer vision.
• Robotics, Robot Operating System, motion planning or industrial automation.
• Agile development with Jira.
• A degree in computer science or a related field is welcome but not required. We care about what you've built.

What success looks like

• 3 months: shipped a new agent tool or skill to production, with an evaluation covering it.
• 6 months: own a fine-tuned or distilled model in the evaluation and release pipeline.
• 12 months: a self-hosted model you built is running in a customer deployment.