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Join a forward-thinking team dedicated to building cutting-edge AI-powered products. As an AI Engineer, you'll play a key role in designing and deploying scalable solutions using modern frameworks and cloud technologies. You'll collaborate with cross-functional teams including product managers, data engineers, software developers, and AI specialists to bring innovative ideas to life.

Key Responsibilities:

• Contribute to the development and deployment of scalable AI solutions across various product lines.
• Work closely with stakeholders to translate business needs into technical implementations.
• Build and optimise GenAI architectures using industry-leading tools and frameworks.
• Develop solutions leveraging cloud-based AI services, particularly within the Azure ecosystem (e.g., Azure OpenAI).
• Support the implementation and refinement of Retrieval-Augmented Generation (RAG) systems, including embedding strategies and indexing techniques.
• Assist in managing evaluation pipelines and frameworks using tools such as Langfuse.
• Document and share best practices related to GenAI and Agentic AI architectures.

Required Skills and Experience:

• Solid background in software engineering or machine learning engineering, with hands-on experience in real-world projects.
• Proven ability to design and deploy production-grade Generative AI solutions.
• Strong programming skills in Python.
• Experience with cloud AI platforms, preferably Azure or AWS.
• Familiarity with GenAI tools and both open-source and commercial LLMs.
• Understanding of advanced AI architectures such as Agentic AI and RAG (including GraphRAG).
• Proficiency with frameworks like LangChain, LlamaIndex, and evaluation tools such as Langfuse.

Are you a Data Scientist with a strong mathematical foundation and a passion for building intelligent systems? We're looking for a seasoned professional to join a forward-thinking tech team tackling complex, high-impact problems using machine learning and statistical modelling.

This is a classic data science role, ideal for someone who enjoys working with large-scale data, designing predictive models, and collaborating with engineering teams to bring algorithms into production.

🔍 What You'll Be Doing

• Collaborate with engineers to integrate advanced algorithms into scalable production systems.
• Design and implement mathematical models and machine learning algorithms for predictive analytics.
• Analyse real-time and historical data to uncover actionable insights.
• Validate and refine models through rigorous statistical analysis.
• Present complex findings to non-technical stakeholders to inform strategic decisions.

🎯 What We're Looking For

• 5+ years of experience in data science or a related field.
• Master's degree (or higher) in Mathematics, Statistics, Computer Science, Physics, or Engineering.
• Strong Python skills, with experience using NumPy, Pandas, and ML frameworks like TensorFlow, PyTorch, or SciPy.
• Proven ability to translate real-world problems into mathematical models and deploy them effectively.
• A curious, versatile mindset - comfortable switching between deep analytical work and hands-on development.

🌟 Bonus Points For

• A PhD in a quantitative field.
• Contributions to open-source ML/statistics projects.
• Experience with simulation modelling or applied statistics.
• Strong database and data modelling experience.

Role - Data Governance Lead

Location -Remote

Start Date - 29th Of September (can wait a week or so after)

Rate - Market Rates (Outside IR35)

Contract Length - 3 Months initially (Will extended based on good performance)

The role is Outside IR35



Description:

I'm looking for a Data Governance expert with a strong background in Metadata, Data lineage, Data Glossaries etc, ideally with Retail Analytics experience. This position is Outside iR35 for a start date of within 2 weeks.



Key Workstreams & Deliverables:

Centralising Metadata: This workstream focuses on establishing a centralised metadata and data lineage system.

• Deliverables: Current state metadata & lineage map, Metadata Management Framework & Standards, two metadata-enriched domains live in a pilot platform, Stakeholder Engagement & Training for metadata, and a gap analysis report on metadata coverage and tool capability.

Data Stewardship Program: This workstream aims to establish and operationalize a robust Data Stewardship program.

• Deliverables: Nominated Data Stewards, Data Steward Handbook, Data Steward Onboarding Material and Process, Drafted Data Lifecycle Process, Training and Activated Data Stewards, and Next Steps and Costed Implementation Plan.

Tiered Governance Framework and Approval Automation: This workstream focuses on formalizing data risk assessment and automating approvals for low-risk data.

• Deliverables: Documented Tiered Data Risk Framework, Mapping of Data Use Cases to Risk Tiers, Automated Approval Workflow for Low-Risk Data, and Updated Training and Documentation for tiered governance.

Please share your CV if you are interested