Data Scientist

Role Overview

• Design and deliver production-grade agentic systems, from multi-agent orchestration to the AI services and models that power them
• Assess fit of agentic solutions per use case, being intentional in system design
• Design the most suitable orchestration pattern per use case
• Evaluate and recommend the best suited agent development framework
• Engineer multi-step agent workflows that are reliable, auditable and modular, supporting both linear and non-linear control flows
• Implement agent memory and state management systems
• Optimise context assembly for each agent interaction
• Evaluate and select the most appropriate foundation models, embeddings and context-specific model variants; apply fine-tuning or adaptation where needed
• Implement guardrails to ensure operation within ethical, legal, brand boundaries
• Build services for agent use, including ML models, GenAI and task-specific APIs
• Process multi-modal data using appropriate embeddings and vector retrieval
• Build, evaluate and maintain robust RAG and / or GraphRAG pipelines
• Implement fallback strategies (e.g., retries, backup tools, escalation, safe exits)
• Build and integrate MCP servers to expose and consume data and AI services
• Use Google's A2A protocol to support independent agent communication, designing composable service boundaries and appropriate integration patterns
• Containerise applications (Docker) – our platform team manages K8s
• Apply AgenticOps practices, including evaluation, observability, guardrails, security, performance optimisation, CI/CD, versioning, rollback, drift detection
• Design and implement testing strategies for non-deterministic behaviour
• Stay abreast of the latest AI research trends and integrate them into our products
• Maintain documentation for architectures, APIs and operational procedures
• Communicate progress, blockers, technical approaches, challenges and potential solutions to technical colleagues and non-technical stakeholders.

Requirements

• Educational background in Computer Science, Data Science, or similar
• Professional experience in Data Science, AI/ML Engineering, Software Engineering, or related technical roles
• High proficiency in Python and working knowledge of SQL
• Experience building API services using FastAPI, Flask or similar
• Experience with containerisation, cloud platforms and CI/CD
• Experience with AgenticOps best practices, including evaluation, observability, safety guardrails, security, performance optimisation
• Experience designing and building complex multi-agent systems using at least one of the major agent frameworks (e.g. ADK, AutoGen, LangGraph)
• Understanding of agent memory and state management patterns
• Familiarity with MCP servers
• Quality-first, test-driven mindset with focus on automated testing
• Deep understanding of LLMs, foundation models and embeddings
• Experience with structured and multi-modal data processing
• Experience building and evaluating production-grade RAG systems
• Practical experience with machine learning / deep learning models
• Excellent communication skills with technical / non-technical audiences
• Strong documentation and knowledge-sharing practices

Tech Stack

• Assembly
• Cloud
• Docker
• Flask
• Kubernetes
• Python
• SQL

Benefits

• enhanced pension
• life assurance
• income protection
• private healthcare
• Remote working
• café, bedroom, beach
• wherever works
• Truly flexible working hours
• school pick up, volunteering, gym
• Generous Leave
• 27 days holiday plus bank holidays and enhanced family leave
• Annual bonus
• when Satalia does well, we all do well
• Impactful projects
• focus on bringing meaningful social and environmental change
• People oriented culture
• wellbeing is a priority, as is being a nice person
• Transparent and open culture
• you will be heard
• Development
• focus on bringing the best out of each other