Senior Talent Acquisition Executive | UK & EUROPE (Failure is the opportunity to begin again more intelligently)
Location: 100% Remote
Job Type: Contract
Job Description
Experience: 10+ years total, with 3–5 years in AI/ML (including GenAI)
About the Role
We are looking for a Staff / Principal Generative AI Engineer to lead the design and implementation of next‑generation AI systems that enhance developer productivity and enable enterprise‑scale AI adoption.
You will architect solutions, guide research‑to‑production workflows, and collaborate with cross‑functional teams across AI, backend, and product engineering.
This role requires deep expertise in foundation models, multimodal architectures, and applied AI — combining strategic vision with strong hands‑on technical execution.
Key Responsibilities
Architecture & Strategy
Define the technical vision and architecture for Generative AI initiatives.
Drive research and engineering convergence for production‑grade GenAI systems.
Evaluate and integrate cutting‑edge LLMs (e.g., GPT, Claude, Gemini, LLaMA, Mistral).
Lead fine‑tuning, RAG (Retrieval‑Augmented Generation), and model evaluation pipelines.
Build scalable training and inference systems leveraging GPUs or distributed compute.
Design agents and frameworks for multi‑model orchestration and context‑aware reasoning.
Collaborate with backend and MLOps teams to ensure robust deployment of GenAI systems.
Implement scalable APIs and vector databases for semantic retrieval and prompt optimization.
Maintain strong performance, latency, and cost trade‑offs for production AI workloads.
Leadership
Mentor senior engineers and guide AI best practices across product teams.
Partner with Product, Research, and Partnerships to explore new AI‑driven opportunities.
Publish internal and external thought leadership on GenAI technologies.
Requirements
Technical Expertise: 10+ years of total software engineering experience, with 5+ in ML/AI systems.
Proficiency in Python, PyTorch, or JAX.
Deep understanding of transformer‑based architectures and LLM fine‑tuning.
RAG systems, embeddings, vector databases (e.g., Pinecone, FAISS, Milvus).
Model compression, quantization, and inference optimization.
Experience with LangChain, LlamaIndex, or similar frameworks.
Solid grasp of distributed training, model evaluation, and observability.
Thanks
Aatmesh
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