AI Engineer

• Reporting directly to our CTO, you'll own the AI systems that turn our Voice of Customer data into insight the whole bank can search, understand, and act on.
• It's a senior, hands-on role with real autonomy. You'll take problems from a rough idea through to systems running reliably and safely in production inside a regulated bank.
• You won't be working through someone else's backlog; you'll help decide what's worth building and then build it.

Senior AI Engineer

• Design, develop, and deploy AI solutions leveraging LLMs (GPT, Claude, open-source models).
• Engineer and optimize prompts to improve accuracy and relevance across use cases.
• Collaborate with cross-functional teams and retail/CPG stakeholders to identify and deliver high-impact AI solutions.
• Monitor, fine-tune, and maintain models to ensure performance, scalability, and business alignment.
• Implement AI/ML-driven efficiencies in BI migration initiatives (e.g., MicroStrategy to Metabase).
• Ensure data governance, quality, and security throughout AI and migration processes.
• Stay current with advancements in the AI/LLM ecosystem to drive continuous innovation.

AI Engineer – Retail, CPG Domain

• Design and implement agentic AI systems (single and multi-agent) with tool use, memory, and fallback mechanisms.
• Build production-grade agents using frameworks like LangGraph, AutoGen, CrewAI, or custom LLM orchestration layers.
• Implement agent reasoning loops including planning, tool selection, execution, observation, and re-planning with safety guardrails.
• Develop prompt and context engineering strategies for reliable, grounded LLM outputs.
• Design agent orchestration workflows include task routing, parallel execution, state management, retries, and human-in-the-loop escalation.
• Build evaluation frameworks for LLMs and agents including automated testing, adversarial testing, and performance benchmarking.
• Implement retrieval and grounding using vector databases, embeddings, and knowledge graphs for contextual accuracy.
• Ensure observability of agent systems by tracing LLM calls, tool usage, and decision paths using monitoring tools.
• Apply security and governance controls including prompt injection defense, access control, and safe tool execution.
• Optimize agent systems for latency, cost, and scalability in production environments.
• Build CI/CD pipelines for agent workflows including versioning, testing, and controlled deployments.
• Integrate agents with enterprise systems and APIs to automate end-to-end business workflows.
• Design feedback loops using production traces and evaluation signals to continuously improve agent performance.
• Experience with Model Context Protocol (MCP) systems to design database connections, integrate APIs, and enable secure tool orchestration for AI agents.
• Hands-on experience in fine-tuning LLMs for domain-specific applications using LoRA, PEFT, QLoRA, RLHF, instruction tuning, and other parameter-efficient adaptation techniques.
• Stay current with emerging agentic AI frameworks, research, and best practices for production deployment.

• Design and implement agentic AI systems (single and multi-agent) with tool use, memory, and fallback mechanisms.
• Build production-grade agents using frameworks like LangGraph, AutoGen, CrewAI, or custom LLM orchestration layers.
• Implement agent reasoning loops including planning, tool selection, execution, observation, and re-planning with safety guardrails.
• Develop prompt and context engineering strategies for reliable, grounded LLM outputs.
• Design agent orchestration workflows include task routing, parallel execution, state management, retries, and human-in-the-loop escalation.
• Build evaluation frameworks for LLMs and agents including automated testing, adversarial testing, and performance benchmarking.
• Implement retrieval and grounding using vector databases, embeddings, and knowledge graphs for contextual accuracy.
• Ensure observability of agent systems by tracing LLM calls, tool usage, and decision paths using monitoring tools.
• Apply security and governance controls including prompt injection defense, access control, and safe tool execution.
• Optimize agent systems for latency, cost, and scalability in production environments.
• Build CI/CD pipelines for agent workflows including versioning, testing, and controlled deployments.
• Integrate agents with enterprise systems and APIs to automate end-to-end business workflows.
• Design feedback loops using production traces and evaluation signals to continuously improve agent performance.
• Hands-on experience in fine-tuning LLMs for domain-specific applications using LoRA, PEFT, QLoRA, RLHF, instruction tuning, and other parameter-efficient adaptation techniques.
• Stay current with emerging agentic AI frameworks, research, and best practices for production deployment

Machine Learning Engineer

• Develop and implement machine learning models and algorithms for large scale machine learning projects.
• Collaborate with data scientists and software engineers to design and deploy scalable machine learning solutions.
• Conduct data analysis and feature engineering to extract valuable insights from large datasets.
• Optimize and fine-tune machine learning models to improve performance and accuracy.
• Stay up-to-date with the latest advancements in machine learning and apply them to enhance existing models.
• Collaborate with cross-functional teams to define project requirements and deliver high-quality solutions on time.

Associate Director, Machine Learning Engineer

• Build and experiment with ML models and LLM-powered systems (classification, embeddings, retrieval, agents).
• Work on real-world use cases such as retail personalization, workflow automation, and customer intelligence.
• Design and improve prompt pipelines, evaluation frameworks, and agent behaviors.
• Collaborate on data pipelines: ingestion, cleaning, feature engineering, and analysis.
• Prototype and ship features quickly in a production environment.
• Contribute to internal tooling for model monitoring, evaluation, and iteration.

Intern – Machine Learning

AI Research Scientist

• Lead the full lifecycle of AI applications from data exploration to deployment
• Architect agent-based reasoning systems for clinical workflows
• Ensure models are explainable and compliant with regulations

Principal AI Scientist

• Design and evolve automation frameworks for public cloud infrastructure deployments at scale.
• Enable secure cloud adoption by automating governance, audit, security, and compliance controls.
• Use, develop and maintain AI services across AWS Platform (Cloud Native)
• Build and maintain infrastructure and application monitoring to detect, prevent, and respond to incidents.
• Drive cloud reliability, performance, and cost optimisation through best practices and continuous improvement.
• Develop tools and mechanisms that eliminate repetitive operational tasks and improve engineering efficiency.
• Review code, assess technical implementations, and provide actionable feedback to improve platform tooling.
• Participate in on-call rotations and serve as an escalation point for cloud service incidents.

Cloud Platform Engineer, AI

• Lead the development of multi-agent workflows and architectures.
• Design model optimization and fine-tuning pipelines (parameter-efficient finetuning, LoRA, quantization).
• Oversee DevOps and MLOps pipelines — CI/CD, model versioning, containerization, and monitoring.
• Collaborate with data science teams on model evaluation and benchmarking.
• Drive production readiness — latency reduction, error recovery, and traceability.
• Mentor the Agentic AI developer team and review their code, design, and deployment.

Lead AI Developer

• Define and drive the research roadmap for core AI capabilities spanning LLMs, ASR/speech understanding, retrieval-augmented generation, and agentic systems.
• Design, train, fine-tune, and evaluate state-of-the-art models, taking them from prototype to robust, production-grade systems.
• Lead the architecture of scalable training and inference pipelines that operate reliably on real customer-scale data.
• Establish rigorous evaluation frameworks, benchmarks, and quality bars for model performance, safety, and reliability.
• Partner closely with product, platform, and ML engineering teams to ship research into customer-facing features.
• Mentor senior and mid-level engineers, set technical standards, and act as a force multiplier across the AI org.
• Stay ahead of the field, evaluate emerging techniques, and represent Level AI's technical work externally where appropriate.

See all jobs on JobTailor

Senior AI Engineer

AI Engineer – Retail, CPG Domain

AI Engineer

AI Engineer

Associate Director, Machine Learning Engineer

Intern – Machine Learning

Principal AI Scientist

Cloud Platform Engineer, AI

Lead AI Developer

Principal AI Research Engineer