Role: Gen AI lead
Location: London, UK (Hybrid)
Employment type: FTC (Fixed Term Contract) Role
* Core AI/ML Foundations:
* Strong foundational knowledge in GenAI, Machine Learning (ML modeling), Data Science, Statistics, and AI fundamentals, including Natural Language Processing (NLP), Neural Networks, and Large Language Models (LLMs).
* Generative AI & LLM Expertise:
* Extensive hands-on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open-source LLMs.
* Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementation.
* Proven ability to build, tune, and deploy LLM-based applications using platforms like Vertex AI, Hugging Face, etc.
* Expertise in developing robust prompt engineering strategies, prompt tuning, and creating reusable prompt templates.
* Hands-on experience with agentic framework-based use case implementation.
* Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.
* Programming & Data Engineering:
* Strong programming proficiency in Python is a must, including extensive experience with libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers, FastAPI, Seaborn, LangChain, and LlamaIndex.
* Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration tools.
* Hands-on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retrieval.
* Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processing.
* Deployment & MLOps:
* Critical: Hands-on experience deploying GenAI-based models to production environments.
* Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipelines.
* Strong expertise in CI/CD principles and tools (e.g., Jenkins, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deployments.
* Cloud & Containerization:
* Proven experience with container orchestration platforms like OpenShift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud-native environment.