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.
• 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.
• 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.

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.

Principal AI Research Engineer

• Work as an Associate Architect - Machine Learning (AWS)
• Collaborate with cross-functional teams to deliver cloud ML solutions
• Implement and develop machine learning models on AWS
• Optimize and evaluate machine learning workflows and models
• Design and maintain software architecture for cloud-based applications

Associate Architect – Machine Learning

• Engineer and maintain innovative, customer-centric, high-performance, secure and robust solutions
• Work within a feature team engineering software, scripts, and tools that are often complex
• Liaise with other engineers, architects, and business analysts across the platform
• Produce complex and critical software rapidly and of high quality which adds value to the business
• Work in permanent teams responsible for the full life cycle from initial development, through enhancement and maintenance to replacement or decommissioning
• Collaborate to optimise software engineering capability
• Design, produce, test, and implement working code
• Work across the life cycle from requirements analysis and design through coding to testing, deployment, and operations

AI Engineer, Advanced Capabilities, AVP

• Define enterprise reference architectures for Agentic AI and LLM-powered platforms, including: single-agent and multi-agent systems, tool-calling and function orchestration, memory, planning, and execution layers.
• Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs, including model selection, deployment patterns, and cost-latency trade-offs.
• Design secure-by-default GenAI systems incorporating guardrails and policy enforcement, data privacy, PII handling, and prompt safety.
• Architect large-scale RAG solutions, covering data ingestion and curation pipelines, chunking and embedding strategies, vector databases and hybrid search, evaluation and feedback loops.
• Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems.
• Drive production readiness of GenAI systems: API-first design (FastAPI / REST / event-driven), CI/CD for LLM workflows, monitoring, evaluation, and cost tracking.
• Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts.
• Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments.
• Act as a technical authority for GenAI across delivery teams and client engagements.
• Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering.
• Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation.

AI Lead, Architect – Claude, LLM Skills

• As a Staff Software Engineer (AI), you will lead the design and delivery of innovative AI solutions, driving technical strategy and embedding within business units to reimagine processes and accelerate TR’s AI-first transformation.
• Design and lead the implementation of complex, enterprise-scale AI systems, including sophisticated multi-component pipelines, advanced retrieval-augmented generation (RAG) architectures, and autonomous AI agents with multi-step reasoning capabilities.
• Architect scalable AI model integration strategies for production-ready prototypes using robust APIs, distributed data pipelines, and microservices architectures.
• Lead the adaptation and customization of AI models for specialized internal domains, driving innovation in business process transformation.
• Define technical standards and best practices for rapid prototyping and solution discovery.
• Contribute directly to hands-on delivery, leveraging AI development tools to rapidly build and iterate on the systems you design.
• Lead evaluation and strategic adoption of cutting-edge AI techniques to solve complex business challenges for internal teams.
• Oversee proof-of-concept projects and guide technical feasibility assessments for new AI-driven features.
• Stay at the forefront of AI research and emerging technologies, translating insights into actionable technical roadmaps.
• Drive innovation initiatives and establish TR’s position in emerging AI technology areas.
• Provide Senior Technical Leadership by leading technical architecture decisions and translating high-level business requirements into comprehensive, scalable technical specifications.
• Foster technical growth and career development of the team and champion MLOps and LLMOps practices in the prototyping and delivery phases.
• Embed directly with prioritized business units to develop deep domain expertise and build stakeholder relationships.

Lead AI Engineer – AI System, AI Frameworks, APIs

• Ship the hardest implementation work yourself — the human-in-the-loop routing, the public/private gateway access controls, the early agent harnesses
• Design and implement the human-in-the-loop routing system: queue mechanics, reviewer assignment, back-pressure handling, run resumption semantics
• Implement the execution wrapper that enforces human-in-the-loop polices at execution time
• Build the safeguards — refusal policies, prompt-injection protections, public/private MCP exposure controls — that make our agents safe to deploy at scale
• Review PRs (human- and code-agent-authored) at a depth that builds shared judgment about what good agent code looks like
• Mentor engineers through hard implementation problems; close gaps in the team's shared knowledge

Lead AI Engineer

• Supporting the delivery of managed services by assisting in the transition and management of service delivery teams
• Engaging in project management tasks to streamline operations and enhance client efficiency
• Contributing to process optimization efforts by implementing automation solutions and frameworks
• Participating in continuous improvement initiatives to refine managed services processes and tools
• Assisting in the development and execution of operational strategies to improve service performance
• Collaborating with team members to address client needs and deliver quality outcomes
• Utilizing data quality automation techniques to enhance service delivery and operational excellence
• Engaging in stakeholder management to maintain effective communication and project alignment
• Applying business process improvement methodologies to optimize client operations
• Supporting the analysis of process performance to identify areas for enhancement and innovation

Agentic AI Engineer – Associate, AI Managed Services

• Managing and optimizing managed services processes to enhance client operations and efficiency
• Leading delivery teams in transitioning and running services, confirming timely and effective execution
• Utilizing project management skills to oversee program performance and delivery risk
• Implementing continuous improvement strategies to refine tools and services within managed services
• Analyzing complex problems and developing innovative solutions to meet client needs
• Building and maintaining meaningful client relationships to support service delivery
• Applying critical thinking to break down complex concepts and inform strategic decisions
• Interpreting data to generate insights and recommendations for process optimization
• Upholding professional and technical standards in alignment with firm guidelines
• Mentoring junior team members to foster growth and development within the team

Senior Associate, Agentic AI Engineer – AI Managed Services

• Lead the end-to-end lifecycle of Agentic AI platform capabilities
• Define and drive key AI platform capabilities
• Partner with engineering, data, security, and product teams to build scalable capabilities
• Own the product strategy for how agents answer questions, assist users, take actions
• Work with business product teams to identify and prioritize high-value agentic use cases
• Drive a platform approach for AI by creating reusable patterns, shared capabilities
• Define AI quality and evaluation requirements
• Collaborate to ensure agentic actions follow proper authentication, authorization
• Drive a developer-centric approach for AI platform services
• Use AI-powered tools to improve discovery, analysis, documentation
• Work with cross-functional stakeholders to align priorities, gather requirements
• Drive KPIs that reflect real-world AI and platform impact

Principal Product Manager – Agentic AI Platform

• Lead research and development of advanced generative AI models (GANs, VAEs, multimodal and agentic AI) for real-world applications
• Design, develop, and deploy scalable AI solutions, optimizing performance, robustness, and production readiness
• Build and maintain end-to-end AI pipelines for data processing, model training, evaluation, and deployment
• Collaborate with cross-functional teams and mentor engineers to drive innovation and deliver impactful AI solutions

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