About the role
LLM Engineer (GenAI)
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.
What You'll Do & The Impact You'll Have
- 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.
- Implement prompt versioning, rollback strategies, and model monitoring to ensure reliability post-deployment.
- Collaborate with cross-functional stakeholders (engineering, data, product, client teams) to deliver high-impact solutions on tight timelines.
What you’ll bring
What You'll Bring to the Table
Must-Have Skills
- 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.
Nice-to-Have Skills
- 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.
Benefits and perks
What We Bring to the Table
- 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.
Skills
Required
ModelBenchmarkingTransformerCloudPython
Nice to have
LambdaDebuggingMilvusTestingDockerPerformance optimizationKubernetesSoftware EngineeringObservabilityConsultingAWSGuardRailsBedrockRollbackOpenSearchrag
Your contact
R
Ryan