Senior AI Engineer

Jobtailor; Singapore; On-site; SGD 180,000 - 230,000; Full time

Job summary

Jobtailor in Singapore is seeking a senior software engineer to build and deploy LLM-powered capabilities end to end. You will design agents that plan and execute multi-step work, implement tool calling, structured outputs, and maintain durable state across services.

You will also build robust retrieval and RAG systems, fine-tune open-weight models on multi-GPU, and establish evaluation loops with automated scoring and tracing.

Qualifications

• Bachelor's or Master's degree in Computer Science or related field.
• 5+ years building and shipping production software with LLM-powered systems.
• Strong Python and track record of shipping reliable services: async, HTTP and streaming APIs, testing, code review.
• Hands-on experience designing and shipping agents: the loop, tools, context, memory, and failure modes; frameworks like LangGraph or equivalent; structured outputs in Pydantic or JSON Schema.
• Experience building RAG systems: embeddings, chunking, hybrid search, reranking.
• Direct experience fine-tuning open-weight models (LoRA, QLoRA, or full-parameter) on multi-GPU, including data curation.
• Experience with LLM evaluation and guardrails: LLM-as-judge or automated scoring, regression tracking, tracing over runs.
• Experience building shared LLM tooling or platform components, owning ambiguous systems in an early-stage environment.

Responsibilities

• Build and ship LLM-powered capabilities end to end: prototype, evaluate, deploy, and iterate into production services.
• Design agents that plan and carry out multi-step work: tool calling, structured outputs, durable state, and judgement to know when an agent is the wrong tool.
• Build retrieval that provides models the right context: ingestion, chunking, embeddings, hybrid search, reranking.
• Fine-tune open-weight models with LoRA, QLoRA, or full-parameter tuning on multi-GPU, curating training data.
• Build evaluation loops that gate what ships: automated scoring, LLM-as-judge, regression tracking against curated test sets.
• Instrument model calls and tool use with tracing to keep quality, cost, and failures debuggable in production.
• Turn LLM capabilities into clean APIs and reusable tooling that other engineers build on.

Skills

LLM Systems

Python Programming

Agent Design

RAG Systems

Model Fine-Tuning

Evaluation & Guardrails

Education

Bachelor's Degree in Computer Science

Master's Degree in Computer Science

Tools

LangGraph

Pydantic

JSON Schema

Multi-GPU Systems

APIs

Job description

• Build and ship LLM-powered capabilities end to end: prototype, evaluate, deploy, and iterate them into production services users rely on.
• Design agents that plan and carry out multi-step work: tool calling, structured outputs, durable state, and the judgment to know when an agent is the wrong tool.
• Build retrieval that gives models the right context: ingestion, chunking, embeddings, hybrid search, reranking.
• Fine-tune open-weight models with LoRA, QLoRA, or full-parameter tuning on multi-GPU, curating the training data and choosing the method by task, compute budget, and target.
• Build evaluation loops that gate what ships: automated scoring, LLM-as-judge, and regression tracking against curated test sets.
• Instrument model calls and tool use with tracing, so quality, cost, and failures stay debuggable in production.
• Turn LLM capabilities into clean APIs and reusable tooling that other engineers build on.

Requirements

• Bachelor's or Master's degree in Computer Science or a related engineering field, and 5+ years building and shipping production software, including deep hands-on work building LLM-powered systems in production.
• Strong Python and a track record of shipping reliable services: async, HTTP and streaming APIs, testing, code review.
• Production experience with LLMs: prompting and context engineering, tool calling, structured output, and the latency and cost work that keeps them usable.
• Hands-on experience designing and shipping agents: the loop, the tools, context, memory, and where they fail. A framework such as LangGraph or equivalent; structured outputs in Pydantic or JSON Schema.
• Experience building RAG systems: embeddings, chunking, hybrid search, reranking, and a feel for what actually moves retrieval quality.
• Direct experience fine-tuning open-weight models (LoRA, QLoRA, or full-parameter) on multi-GPU, including curating and formatting the training data.
• Experience with LLM evaluation and guardrails: LLM-as-judge or automated scoring, regression tracking, and tracing over agent runs.
• Experience building shared LLM tooling or platform components that other engineers build on, and comfort owning ambiguous systems end to end in an early-stage environment.

Core Competencies

Demonstrates expertise in building and deploying LLM-powered systems, including fine-tuning models and designing agents for multi-step tasks. Proficient in Python and experienced in creating reliable APIs and tooling for production environments.

Highest-signal resume keywords

• LLM-Powered Systems Development
• Python Programming
• Model Fine-Tuning (LoRA, QLoRA)
• Agent Design and Implementation
• RAG Systems Building

ATS Optimization Keywords

Hard Skills

• Python
• LLM Fine-Tuning
• Model Evaluation
• Context Engineering
• Tool Calling
• Structured Output
• Embeddings
• Chunking
• Hybrid Search
• Regression Tracking

Certifications & Qualifications

• Bachelor's Degree in Computer Science
• Master's Degree in Computer Science

Industry Keywords

• Production Software
• LLM Evaluation
• Automated Scoring
• Retrieval Quality
• Debugging in Production

Tools & Technologies

• LangGraph
• Pydantic
• JSON Schema
• Multi-GPU Systems
• APIs