IT

Senior Front-End Developer (React, GraphQL, Amazon Neptune)

Fully Remote (UK-based)

Initial 3-month contract

Up to £450 per day (Negotiable)

We're working with a major UK organisation building a next-generation knowledge platform that combines structured data, graph technology, and AI-enabled workflows.

This is a senior front-end role focused on developing high-performance interfaces that interact directly with a semantic knowledge graph platform.

Key Responsibilities

• Build and maintain complex React/Next.js applications in a modern TypeScript codebase
• Develop rich UI experiences powered by GraphQL APIs connected to Amazon Neptune
• Create advanced Search & Discovery interfaces, combining keyword and AI/vector-based retrieval
• Build dashboards supporting human-in-the-loop validation of AI-generated outputs
• Ensure strict adherence to WCAG 2.2 AA accessibility and design system standards
• Collaborate closely with backend engineers, data teams, and UX/service designers
• Integrate front-end applications with AWS-hosted services (Lambda, API Gateway, ECS)

Core Skills Required

• Strong front-end engineering experience with React or Next.js + TypeScript
• Proven expertise consuming and delivering applications built on GraphQL APIs
• Hands-on experience working with GraphQL backed by Amazon Neptune is essential
• Strong state management skills (Redux, TanStack Query, or similar)
• Deep practical accessibility experience (WCAG standards)
• UK-based and able to meet security and governance requirements

Desirable Experience

• Knowledge graph or semantic data awareness (RDF, JSON-LD, linked data concepts)
• Experience building high-performance search UIs (OpenSearch, Elastic, vector search)
• Familiarity with CI/CD pipelines and Infrastructure-as-Code (Terraform/CDK)
• Interest in AI-enabled workflows and validation tooling

Please note: GraphQL experience specifically with Amazon Neptune is an absolute minimum requirement. Applications without this will not be considered.

Machine Learning Engineer - Contract (12 Months)

Location: Remote (India)
Contract Length: 12 months

We are looking for an experienced Machine Learning Engineer to join a global data and analytics programme, supporting the development of predictive automation solutions using large-scale commercial datasets. This role focuses on building, deploying, and optimising machine learning models on an enterprise data platform, with a strong emphasis on PySpark and distributed data processing.

This is a hands-on engineering role suited to someone who enjoys working with complex, multi-source data and translating advanced analytics into real-world commercial impact.

What you'll be doing:

• Designing and implementing machine learning and deep learning models using PySpark on an enterprise data platform.

• Solving complex analytical problems using large, distributed commercial datasets.

• Building and maintaining end-to-end ML pipelines, from data ingestion through to deployment and monitoring.

• Collaborating closely with commercial, analytics, and data engineering stakeholders to align technical solutions with business goals.

• Identifying data drift, bias, and performance issues, and ensuring models generalise effectively in production.

• Delivering actionable outputs such as segmentation, targeting, and optimisation recommendations.

• Keeping up to date with advances in machine learning, big data, and automation technologies.

Key responsibilities:

• Ingesting, cleaning, and transforming large-scale datasets using PySpark.

• Developing, evaluating, documenting, and monitoring ML models in a production environment.

• Designing and optimising ETL and data pipelines to support scalable ML operations.

• Working with stakeholders to define requirements and translate business objectives into technical solutions.

• Customising or extending ML libraries (e.g. PySpark MLlib) to maximise analytical value.

Skills & experience required:

• Strong experience working with large-scale, distributed data sets.

• Advanced knowledge of statistics, probability, algorithms, and machine learning concepts.

• Hands-on experience with PySpark and big data processing.

• Strong Python development skills.

• Experience with enterprise data platforms and data modelling.

• Excellent communication and collaboration skills.

• Proactive mindset with a willingness to learn new tools and technologies.

Desirable experience:

• Background in machine learning within pharmaceutical, life sciences, or commercial analytics environments.

• Practical experience deploying ML solutions on enterprise data platforms.

• Exposure to additional ML frameworks such as PyTorch or Keras (nice to have).

• Bachelor's or higher degree in computer science, mathematics, engineering, or a related quantitative field.

• Leading a team of data engineers while staying hands-on with design and coding
• Owning the technical direction and architecture of data pipelines and platforms
• Building and maintaining production-grade data workflows using Python, SQL, Snowflake, and DBT
• Designing cloud-based data solutions on AWS using serverless and event-driven patterns
• Setting engineering standards around code quality, testing, CI/CD, and documentation
• Ensuring data platforms are reliable, observable, and cost-efficient in production
• Working closely with business and technical stakeholders to turn requirements into working solutions
• Mentoring engineers and raising the overall engineering bar

• Strong experience in Python and SQL, with a software engineering mindset
• Proven hands-on experience with Snowflake and modern data modelling practices
• Solid background in data engineering (ETL/ELT, orchestration, data quality)
• Practical experience building data systems on AWS
• Experience defining architecture and making technical trade-offs
• Confidence leading technical initiatives and supporting other engineers
• Strong communication skills and comfort working with non-technical stakeholders

• Experience with workflow orchestration tools (e.g. Airflow)
• Exposure to APIs, streaming data, or real-time processing
• Familiarity with regulated or compliance-heavy environments
• Experience improving platform reliability, performance, or cloud costs

• High-impact position with real ownership and influence
• A balance of leadership and hands-on technical work
• Opportunity to shape long-term data architecture and standards
• Collaborative environment with complex, real-world data challenges