Machine Learning Engineer

• Implement machine learning models and data pipelines based on guidance from senior engineers and scientists
• Assist in converting research prototypes into reliable, scalable production workflows
• Support development of data pipelines and feature engineering workflows
• Contribute to model training, evaluation, and performance tuning
• Deploy and monitor ML models in batch and near real-time environments
• Debug issues in data pipelines and model performance with support from senior team members
• Write clean, maintainable, and well-documented code
• Collaborate with software engineers to integrate ML components into production systems

Machine Learning Engineer I

AI Engineer

• Innovate and oversee the AI capabilities powering reactive, proactive, and predictive customer support across all products and solutions.
• Analyze customer issues and risk patterns to embed predictive and preventative AI capabilities directly into the support lifecycle.
• Lead the ideation, prototyping, and scaling of AI-driven solutions from Proof of Concept (PoC) to full production.
• Collaborate closely with engineering, operations, product, and customer-facing teams to implement, optimize, and align solutions with business objectives.
• Continuously evaluate new AI tools and technologies to improve efficiency, accuracy, scalability, and data compliance.
• Act as a mentor within a network of AI and technical experts, sharing best practices, lessons learned, and driving value realization and adoption.

AI Architect

• Lead UX/UI design initiatives in close collaboration with product and engineering teams, with a strong focus on human-centered design and usability
• Use AI-powered tools (for example, Figma AI, Cursor, Claude, and other generative tools) to generate, explore, and refine design concepts, interactions, and UI components
• Create core UX deliverables, including user flows, wireframes, high-fidelity mockups, and interactive prototypes
• Translate design concepts into interactive prototypes that clearly demonstrate form, function, and behavior
• Conduct user research and usability testing to validate design decisions and iterate based on qualitative and quantitative feedback
• Define, document, and evolve information architecture and interaction patterns to improve clarity and ease of use across products
• Collaborate closely with developers to ensure design feasibility, smooth hand-offs, and high-quality implementation
• Contribute to basic front-end prototypes when needed to support exploration and feasibility discussions
• Help maintain, evolve, and scale design systems to ensure consistency across multiple products and platforms

Senior UX/AI Engineer

• Design, build, and maintain AI-powered applications, services, and integrations as part of the AI Engineering team.
• Implement solutions focused on AI agents, agentic workflows, automation, LLM-based applications, and AI-assisted business processes.
• Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django) or equivalent frameworks, React frontends, and relevant AI/ML frameworks.
• Implement AI solutions using AWS AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS services for model hosting, inference, orchestration, data processing, monitoring, and security.
• Work closely with the AI Tech Lead to align on architecture, technology choices, engineering standards, AI patterns, and rollout approaches.
• Provide technical input and guidance to other engineers on AI implementation patterns, code quality, testing, observability, and production readiness.
• Develop and integrate AI agents that interact with internal APIs, business workflows, enterprise systems, knowledge bases, and external tools in a safe and controlled way.
• Build and maintain RAG-based solutions, including document ingestion, chunking, embeddings, vector search, retrieval logic, reranking, and grounding techniques.
• Support the development and deployment of machine learning models and AI solutions into production environments.
• Contribute to ML pipelines and MLOps practices, including data preparation, model training, experiment tracking, model deployment, monitoring, evaluation, and lifecycle management.
• Integrate LLMs through APIs.
• Implement AI evaluation approaches for LLM outputs, RAG quality, agent behavior, model performance, hallucination detection, safety, and reliability.
• Support prompt engineering, prompt versioning, function calling, tool use, memory patterns, guardrails, and LLM application testing.
• Design and consume APIs and contribute to cloud-based, scalable backend architectures.
• Collaborate with product managers, engineers, data scientists, DevOps, security, and business stakeholders to deliver practical AI solutions.
• Write clean, maintainable, testable, and well-documented code.
• Support production rollouts, troubleshooting, monitoring, optimization, and continuous improvement of AI systems.
• Stay current with modern AI technologies, frameworks, models, and engineering practices, and bring practical recommendations to the team.

Senior AI Engineer

• As an AI Engineer, you'll design, build, and operate production-grade AI/ML systems - from agentic applications and MCP-powered tools to the underlying MLOps infrastructure and data foundations that make them reliable at scale
• Your work will power intelligent products and internal automation across the company, and will help shape how the organisation safely adopts AI at scale
• Design and build agentic systems: single agents, multi-agent orchestration, and sub-agent patterns for complex workflows (planner/executor, supervisor/worker, hierarchical task decomposition)
• Develop MCP (Model Context Protocol) servers and tools that expose internal systems, datasets, and actions to LLM-powered applications in a safe, governed, and reusable way
• Implement retrieval systems (RAG, hybrid search, graph-based retrieval) including chunking, embedding, re-ranking, and context-assembly strategies
• Build and maintain MLOps automation: CI/CD for models and agents, environment management, artifact handling, and versioning of prompts, models, data, and code
• Implement observability for AI systems: tracing, token/latency/cost metrics, quality and drift monitoring, alerting, and incident response
• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations
• Expose well-modelled, governed datasets and APIs that agents, tools, and downstream consumers can rely on
• Ensure secure data handling and compliance with relevant data protection standards and internal policies
• Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes

• Design and develop agentic AI systems that can retrieve relevant information and interact with tools and services to support and automate operational workflows in cloud environments
• Design and implement agentic AI systems capable of reasoning, planning, and executing tasks across complex operational environments
• Develop AI agents that can interact with tools and services, including APIs, monitoring systems, knowledge bases, and operational platforms
• Build mechanisms for context and knowledge retrieval, enabling agents to access documentation, historical data, and structured information required for decision-making
• Implement autonomous or semi-autonomous workflows where AI agents assist humans by analysing situations, proposing actions, and executing tasks where appropriate
• Develop scalable backend services and interfaces that allow agents to interact with enterprise systems and cloud platforms
• Deploy and operate agent-based solutions in cloud-native environments, ensuring reliability, observability, and maintainability

• Build the ML stack from the ground up
• Detecting anomalies in API traffic
• Own the full lifecycle
• Design experiments and metrics
• Work with text and structured behavioral data
• Leverage LLMs where they add value
• Shape the technical direction

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