LLM Engineer

TECHKNOWLEDGEY PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

TECHKNOWLEDGEY PTE. LTD. is seeking a Deep Learning Architect with 3–4 years of experience to design and build production-grade GenAI systems. You will guide architecture decisions, review system designs, and contribute to end-to-end LLM and RAG pipelines with a strong emphasis on production readiness.

You will pre-train and fine-tune LLMs using CPT, SFT, LoRA, and QLoRA for client-specific use cases, and optimize models for latency and cost while ensuring robust deployment and governance.

Qualifications

• 3–6 years of ML engineering, LLMs, or model development roles.
• Hands-on pre-training/fine-tuning with CPT, SFT, LoRA, or QLoRA.

Responsibilities

• Design end-to-end LLM system architecture for production use.
• Pre-train, fine-tune LLMs and domain models for client use cases.
• Design and run model evaluation pipelines to benchmark performance and cost.
• Optimise latency, throughput, and inference cost in production.
• Integrate fine-tuned models into multi-agent pipelines.
• Implement prompt versioning and monitoring for reliability.

Skills

Python programming

LLM/transformer knowledge

Model evaluation pipelines

Production readiness

Troubleshooting and debugging

Tools

Bedrock

OpenSearch

AWS Lambda

ECS/EKS

Job description

We are seeking a Deep Learning Architect with 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.

Sounds great – what will I 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.

Ideal Experience

• 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.

What’s in it for me?

• Impact-first culture where your work ships to production and drives measurable business outcomes.
• Professional growth through mentorship, continuous learning, and exposure to diverse enterprise environments.
• A high-performing team that values curiosity, integrity, ownership, and engineering excellence.
• Opportunities for regional and international exposure as projects require.