AI/ML Engineer

E-Solutions; United Kingdom; Remote; GBP 60,000 - 80,000; Part time

Job summary

A technology solutions firm is seeking an AI/ML Engineer to design and implement Generative AI solutions using Google Cloud Platform. The role requires a strong background in AI/ML engineering, with a focus on developing and fine-tuning models. Candidates should possess excellent problem-solving and collaboration skills, and have substantial experience with GCP services. This is a remote contract position.

Qualifications

• 5+ years of experience in AI/ML engineering, 2+ years on Generative AI.
• Hands-on experience with GCP AI/ML services, especially Vertex AI.
• Solid understanding of core AI/ML concepts and deployment principles.

Responsibilities

• Design and implement end-to-end Generative AI solutions.
• Lead fine-tuning and customization of large language models.
• Manage scalable AI/ML models in production environments.

Skills

Generative AI expertise

Google Cloud Platform (GCP)

Python proficiency

Data pipeline construction

Deep learning knowledge

Collaboration skills

Education

Bachelor's degree in Computer Science or related

Tools

TensorFlow

PyTorch

Vertex AI

Job description

Overview

Job Title - AI/ML Engineer

Location - UK

Mode - Remote

Type - Contract (inside IR 35)

Responsibilities

• Generative AI Solution Design & Development
  • Design and implement end-to-end Generative AI solutions, including natural language generation (NLG), image generation, code generation, and multi-modal AI applications, on Google Cloud Platform.
  • Leverage and integrate pre-trained Generative AI models from Google's Model Garden and other sources, and adapt them to specific business needs.
  • Implement advanced prompt engineering and RAG (Retrieval-Augmented Generation) strategies to enhance model performance and accuracy.
  • Explore and integrate external tools, APIs, and open-source models to extend Generative AI capabilities.
• Custom GenAI & Model Fine-tuning
  • Lead the fine-tuning, customization, and adaptation of Large Language Models (LLMs) and other generative foundation models using private or proprietary datasets on Vertex AI.
  • Apply techniques such as LoRA (Low-Rank Adaptation), PEFT (Parameter-Efficient Fine-Tuning), and other advanced methods for efficient model adaptation.
  • Experiment with different generative architectures (e.g., Transformers, Diffusion Models) and identify optimal approaches for specific use cases.
  • Develop and implement data pipelines for preparing high-quality datasets for model training and fine-tuning, utilizing GCP data services like BigQuery and Dataflow.
• AI/ML Engineering & MLOps
  • Build, deploy, and manage scalable and robust AI/ML models in production environments using Vertex AI Endpoints and other deployment strategies.
  • Implement MLOps best practices, including CI/CD for ML models, model versioning, lineage tracking, and automated retraining pipelines (e.g., Vertex AI Pipelines, Kubeflow).
  • Monitor model performance, drift detection, and data quality in real-time using Vertex AI Monitoring and other GCP Operations Suite tools.
  • Optimize AI/ML models for performance, cost-efficiency, and latency on GCP infrastructure, including specialized hardware like GPUs and TPUs.
• Research & Innovation
  • Stay abreast of the latest advancements in Generative AI research, Large Language Models, and the broader AI/ML landscape.
  • Evaluate new Google Cloud AI/ML services, features, and third-party tools, recommending their adoption to enhance capabilities.
  • Contribute to internal knowledge sharing, best practices, and the development of reusable components for Generative AI solutions.
• Collaboration & Communication
  • Work closely with data scientists, product managers, software engineers, and business stakeholders to translate complex requirements into actionable Generative AI solutions.
  • Clearly articulate technical concepts, model limitations, and solution trade-offs to both technical and non-technical audiences.
  • Ensure responsible AI practices are integrated into all solutions, addressing ethical considerations, bias, and fairness.

Required Skills

• Bachelor's degree in Computer Science, Machine Learning, AI, or a related quantitative field, or equivalent practical experience.
• 5+ years of experience in AI/ML engineering, with at least 2+ years specifically focused on Generative AI (LLMs, diffusion models, etc.).
• Strong hands-on experience with Google Cloud Platform (GCP) AI/ML services, especially Vertex AI (Workbench, Training, Prediction, Pipelines, Feature Store).
• Proficiency in Python and strong experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, JAX, Hugging Face Transformers).
• Experience with data engineering and building robust data pipelines for AI/ML workloads on GCP (e.g., BigQuery, Dataflow).
• Solid understanding of core AI/ML concepts, including deep learning, neural networks, NLP, and machine learning algorithms.
• Familiarity with MLOps principles and tools for deploying, managing, and monitoring ML models in production.
• Excellent problem-solving skills, with a passion for building innovative and impactful AI solutions.
• Strong communication and collaboration skills.

Good to have Skills

• Master's degree or Ph.D. in a relevant AI/ML field.
• Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect certification.
• Hands-on experience with custom fine-tuning of LLMs (e.g., using LoRA, PEFT) and prompt engineering techniques.
• Experience with multi-modal Generative AI models.
• Familiarity with vector databases and vector search technologies (e.g., Vertex AI Vector Search) for RAG implementations.
• Experience with serverless computing (e.g., Cloud Functions, Cloud Run) for deploying AI services.
• Familiarity with ethical AI principles, fairness, accountability, and transparency in AI systems.
• Contributions to open-source AI/ML projects or research publications in Generative AI.