AIML Engineer

AIML Engineer (LLM & Client-Facing)

Work Mode:
Hybrid

Location:

London

• Architect and implement advanced LLM systems (RAG, fine-tuning, agents, copilots) from concept to production.
• Design end-to-end AI pipelines encompassing data processing, model evaluation, deployment, and monitoring.
• Select and optimize the use of commercial and open-source LLMs (e.g., GPT, Claude, LLaMA) for performance, cost, and security.
• Build scalable, secure, and cost-efficient cloud-based AI architectures.

• Act as the primary technical point of contact for enterprise clients, translating business needs into technical requirements and solutions.
• Lead workshops, solution walkthroughs, and presentations for both technical and executive audiences.
• Own project delivery timelines, manage risks, and ensure projects are delivered on time and within scope.

• Mentor and coach junior and mid-level data scientists and ML engineers.
• Contribute to hiring, onboarding, and the establishment of technical standards and best practices.
• Lead and scale a high-performing AI delivery team, fostering a culture of excellence.
• Establish robust practices for code quality, documentation, model governance, and MLOps.

• 6+ years of professional experience in data science, machine learning, or AI engineering.
• Proven, hands‑on experience building and deploying LLM applications (RAG, agents, fine‑tuning) in production environments.
• Expertise in Python and deep learning frameworks (PyTorch or Tensor Flow).
• Deep technical knowledge of prompt engineering, retrieval‑augmented generation (RAG) architectures, fine‑tuning, and inference optimization.
• Solid experience deploying and monitoring models in production on a major cloud platform (AWS, Azure, or GCP).
• Proven client‑facing, consulting, or stakeholder management experience with excellent communication and presentation skills.

• Experience directly mentoring or leading technical teams.
• Strong MLOps skills (CI/CD, containerization, model monitoring) using tools like MLflow, Weights & Biases, etc.
• Experience working in regulated industries (finance, healthcare, enterprise SaaS).
• Knowledge of AI governance, responsible AI, data privacy, and security standards.
• Experience scoping projects, estimating timelines, and writing technical proposals.

• Successfully deliver multiple end-to-end LLM projects for key clients, achieving defined success metrics.
• Become the go-to trusted AI advisor for a portfolio of clients and internally for company leadership.
• Design and socialize reusable frameworks, patterns, and best practices that elevate our team's efficiency and quality.
• Take on clear leadership responsibilities for a growing team, contributing directly to their development and the team's productivity gains.

artificial intelligence, langchain, large language models (llms), azure, amazon web services (aws), tensor flow, pytorch, mlops, ci/di, ai, gcp, rag