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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.

If this excites you, please reach out to the PaymentGenes team directly via LinkedIn, who can provide more detail and insight into this company's journey into AI and the opportunity.

If you are interested in applying for this job please press the Apply Button and follow the application process. Energy Jobline wishes you the very best of luck in your next career move.