Job Description
PaymentGenes is proud to be partnering with a high‑growth, international technology organisation to appoint a Senior / Lead Data Scientist (AI-, AWS ML Stack, Production‑Focused).
This is a strategic hire within a business scaling real‑world AI solutions — moving beyond experimentation into production‑grade, AI‑powered systems embedded directly into enterprise workflows.
If you are passionate about deploying scalable ML systems that deliver measurable commercial impact, this opportunity is for you.
This role goes beyond model experimentation! You will design, deploy, and scale AI‑driven solutions using modern foundation models and AWS‑machine learning infrastructure. From LLM‑powered agents to predictive models embedded in automated workflows, your work will directly influence business operations at scale.
You’ll operate at the intersection of modelling, engineering, and intelligent automation.
What You’ll Do
Model Development & AI Systems Design
- Design and train predictive models using AWS SageMaker
- Develop LLM‑powered systems via AWS Bedrock (including Claude integration)
- Build RAG pipelines combining structured and unstructured data
- Develop evaluation frameworks for accuracy, bias, and robustness
- Apply best practices in feature engineering and experimentation
AI Agent & Workflow Integration
- Architect reasoning agents using advanced foundation models
- Use code‑tooling for automation logic and integration scripting
- Orchestrate multi‑step AI workflows
- Deploy AI‑powered decision layers into enterprise processes
- Design human‑in‑the‑loop feedback systems to improve performance
☁️ AWS ML Infrastructure
- Deploy and manage models using SageMaker (training, endpoints, pipelines)
- Leverage Bedrock for foundation model access
- Implement serverless inference with Lambda & API Gateway
- Utilise S3, Glue, Athena for data processing
- Implement CI/CD for ML workflows
- Monitor performance via CloudWatch and drift detection tooling
- Optimise inference cost and latency
Productionisation & MLOps
- Build reproducible ML pipelines
- Implement model versioning and dataset tracking
- Design structured output validation and guardrails
- Monitor performance and trigger retraining cycles
- Ensure governance, compliance, and security alignment
Business Impact & Leadership
- Identify high‑impact AI use cases
- Translate business problems into ML system designs
- Lead experimentation frameworks (A/B testing, uplift modelling)
- Mentor data scientists and collaborate closely with data engineering
- Communicate AI strategy and risk to senior stakeholders
Technical Environment
Core Data Science
- Advanced Python & SQL
- Statistical modelling & ML algorithms
- Feature engineering
- Experiment design & evaluation
AWS ML Stack
- SageMaker (training, endpoints, pipelines)
- Bedrock (foundation models incl. Claude)
- Lambda (serverless inference)
- S3, Glue, Athena
- CloudWatch
- IAM & security best practices
AI‑ Tooling
- Foundation models for reasoning workflows
- Code‑tooling for automation scripting
- Agent orchestration frameworks
- Enterprise workflow automation tools
- RAG architectures
- Embeddings & vector stores
What We’re Looking For
- 6–10+ years in data science or applied ML
- Proven experience deploying ML models into production
- Hands‑on experience with AWS‑ML services
- Experience building LLM‑powered workflows or AI agents
- Demonstrated delivery of measurable business impact
You’ll Thrive If You Have:
- Strong problem‑framing ability
- Systems‑level thinking beyond model accuracy
- AI governance awareness
- Clear communication across technical and executive audiences
- A bias toward practical deployment over research‑only outputs
Example Projects You Might Deliver
- Production fraud detection model deployed via SageMaker endpoint
- Internal AI copilot powered by Bedrock and embedded into workflows
- RAG‑based compliance monitoring assistant
- Automated revenue forecasting pipeline with retraining triggers
- AI‑driven document intelligence system (classification + extraction)
What Success Looks Like
- Reduced time from model prototype to production
- Stable, monitored ML endpoints delivering measurable ROI
- Improved decision accuracy in automated workflows
- Strong adoption of AI‑enabled tools across the business
- Controlled infrastructure cost per inference
This is a rare opportunity to build AI systems that operate at real scale within a forward‑thinking technology environment.
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