Role overview
We are seeking a highly experienced
LLM Engineer Contractor
to support short-term, high-impact work focused on building, optimizing, and deploying large language models at one of our Big Four clients. The ideal candidate has deep technical expertise across modern LLM architectures, inference systems, and applied machine-learning workflows. This role is fast-paced and hands-on, requiring strong research skills, engineering excellence, and the ability to collaborate across product, data, and ML operations teams. The contractor will contribute directly to model development, performance optimization, and the deployment of LLM-backed features into production environments.
What you'll work on
- Model Development & Optimization:
- Design, train, fine-tune, and evaluate LLMs for performance, efficiency, safety, and reliability.
- Optimize models through techniques such as transfer learning, RLHF, low-rank adaptation (LoRA), quantization-aware training, and distillation.
- Conduct rigorous benchmarking and ensure alignment with product or research objectives.
- Systems Integration & Deployment:
- Build scalable inference pipelines that support high-volume, low-latency LLM serving.
- Implement infrastructure optimizations including quantization, caching, sharding, and model distillation.
- Integrate models into applications, APIs, or microservices and collaborate with ML Ops to ensure robust deployment.
- Research & Cross-Functional Collaboration:
- Lead experimentation on new model architectures, prompting strategies, retrieval-augmented generation (RAG), and hybrid search pipelines.
- Work closely with product managers, data engineers, ML ops, and research teams to convert experimental insights into production features.
- Document findings, communicate results, and contribute to technical roadmaps.
What we're looking for
- Experience with RAG systems, vector databases, and search frameworks (e.g., FAISS, Milvus, Pinecone).
- Familiarity with model evaluation for alignment, safety, hallucination reduction, and adversarial testing.
- Prior experience in a research-oriented or applied AI lab environment.
- Master’s degree or Ph.D. in a relevant technical field.
Tags & focus areas
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