AI Engineer

• Design, build, and operate systems in customer-facing production environments
• Translate ambiguous business and customer problems into prototypes & technical specs
• Track AI capabilities and apply them where they create clear leverage
• Own the full application lifecycle: product discovery, experimentation, evaluation, deployment, and monitoring
• Own system design, tradeoffs, and long-term scaling and maintainability
• Build code bases that are legible to agents and other developers; drive the organization forward on tooling
• Partner with product managers to enhance their product vision, including with AI-native solutions

• Architect and develop enterprise-scale multi-agent systems leveraging LLMs and autonomous agent frameworks using Google ADK, Agentspace, MCP, RAG, and A2A orchestration
• Design and implement RAG pipelines using BigQuery and Vertex AI Engine for knowledge grounding and factually accurate responses
• Optimize agents for orchestration, knowledge grounding, multi-step reasoning, and decision-making
• Design and implement distributed training workflows, online inference systems, and low latency serving architectures optimized for real-world performance, using Google cloud-native services
• Engineer scalable, secure, compliant and production-grade AI fabric and AI agent workflows using Vertex AI and modern cloud-native technologies
• Create reusable agent orchestration layers, observability hooks, and governance frameworks that accelerate Agentic AI adoption across TAG brands
• Partner with cross-functional stakeholders in translating business requirements into technical specifications
• Own the full AI development lifecycle – from data collection and implementation to deployment and monitoring
• Implement intelligent observability and automation strategies to ensure AI system reliability and performance at scale

Staff AI Engineer

• Explore and evaluate emerging AI tools, platforms, and agent-based solutions to identify opportunities for the business.
• Design, build, and integrate AI-powered workflows, services, and applications into enterprise systems.
• Help shape and develop internal AI platforms, including agent architectures, permissions, and governance guardrails.
• Guide AI initiatives through structured evaluation and approval processes, ensuring alignment with technical and governance standards.
• Collaborate with teams across Architecture, Security, DevOps, Legal, Privacy, and the wider business to deliver well-rounded AI solutions.
• Translate complex or ambiguous business needs into clear technical requirements, success metrics, and deliverables.
• Monitor performance, adoption, and usage of AI solutions, identifying trends, risks, and opportunities for optimisation.
• Maintain clear documentation, including AI use cases, tool registries, and lifecycle records, ensuring audit-ready standards.
• Support the development of a centralised AI knowledge base to drive consistency and accelerate adoption across teams.
• Contribute to continuous improvement of AI governance, development practices, and delivery frameworks.

AI Engineer, AI Analyst

• Design and develop agent components and templates for the Collaboratory
• Lead small-scale implementation projects, coordinating with stakeholders and team members
• Create and maintain standardized documentation and “recipe cards” for AI solutions
• Conduct quality assurance testing and debugging of agent components
• Analyze usage metrics and user feedback to improve solution offerings
• Facilitate user training sessions and support enablement events
• Maintain version control documentation and update logs for Collaboratory items
• Assist in packaging common support requests into reusable agent components
• Support junior analysts and contribute to team knowledge sharing

GenAI Engineer – Associate

• Evaluate emerging AI tools, platforms, and agents
• Conduct structured technical assessments
• Build and integrate AI workflows and services into enterprise systems
• Collaborate with Architecture, Security, DevOps, Legal, Privacy, and business stakeholders
• Monitor adoption metrics, cost and consumption trends, quality indicators, and post-release performance

AI Engineer / AI Analyst

• Rapidly prototype AI agents and guided workflows that solve concrete customer problems; ship thin slices, instrument, and iterate with real usage.
• Design and implement cloud-native backend services that orchestrate prompts, tools, retrieval, and automations; own the path from POC to production.
• Build robust retrieval for LLMs (search/vector pipelines, context assembly, tool selection) in partnership with data engineering.
• Establish evaluation loops: offline/online tests, golden sets, regression checks, and human-in-the-loop review to measurably improve quality and safety.
• Contribute to developer experience: reusable agents/tools, SDKs, templates, and internal documentation that speed up future builds.
• Collaborate with customers and internal stakeholders to frame problems, define success metrics, and translate insights into productizable solutions.
• Lead design discussions and code reviews; mentor teammates and raise the quality bar while keeping momentum high.

• Lead the development of advanced AI and machine learning systems
• Design end-to-end AI solutions, optimize model performance
• Mentor junior engineers and support cross-team collaboration
• Document architectural decisions and best practices

Senior AI Engineer

• Define and own end-to-end enterprise architecture for a Microsoft-based customer engagement and CCaaS platform
• Establish target-state architecture across sales, service, channels, integrations, data, and security
• Develop architecture principles, standards, patterns, and governance guardrails
• Architect solutions across Dynamics 365 Sales, Customer Service, and Omnichannel
• Design end-to-end customer and seller journeys including Lead-to-opportunity, Opportunity-to-close, Account management, Case management and resolution
• Define architecture for: Sales automation, Case management and service workflows, Routing, queues, workstreams, and skills-based distribution
• Architect solutions leveraging Power Platform, Microsoft Teams and Microsoft 365
• Define patterns for integration with third-party and ISV platforms
• Define AI governance including prompt design, security, and responsible AI controls
• Organize and oversee architecture authority across programs and workstreams.

Dynamics 365 Enterprise AI Architect

• Work as senior technical role on a delivery pod
• Review what the AI produces
• Decide what to build

• Design and build agent harness infrastructure: the scaffolding that wraps LLM calls, manages tool use, handles retries, enforces policy, and feeds results back into iterative improvement loops.
• Implement agentic loop patterns with multi-turn reasoning, tool orchestration, memory management, and structured output handling as reusable platform primitives
• Build the agent flywheel: automated pipelines that collect agent traces, surface regressions, route failures to evaluation, and close the loop from production signal back to prompt/model improvement
• Own the end-to-end lifecycle from agent experiment to production deployment, including versioning, rollout controls, and rollback mechanisms
• Build sandboxed execution environments for agent tools with isolating code execution, API calls, and file system access so agents can act without unconstrained blast radius
• Design tiered autonomy models: define which actions agents can take automatically, which require human approval, and which are off-limits and enforced at the infrastructure layer
• Implement replay and dry-run capabilities so new agent versions can be tested against real traces before going live
• Implement evaluation frameworks for agent behavior using a combination of vendor, open source or in house built tools — covering task success, tool selection accuracy, trajectory evaluation, hallucination rates, latency, and cost
• Build and maintain eval datasets, golden trace libraries, and regression test suites that run automatically on every agent code change
• Instrument agent traces end-to-end: LLM calls, tool invocations, intermediate reasoning, final outputs — surfaced in Grafana or equivalent observability tooling
• Define and track agent quality metrics over time; own the signal that tells the team whether agents are getting better or worse
• Drive continuous quality, latency, and cost improvements across deployed agents by closing the loop between production traces, evaluations, and agent design. Optimization may be done through a variety of techniques e.g. prompt tuning, tool calling optimizations, context engineering, right-sizing model selection per task and explore distillation or fine-tuning (SFT, DPO, RLHF) on curated trace data to name a few
• Validate every optimization through A/B tests, shadow deployments, and replay against golden traces, with the eval suite gating rollout so wins are real and regressions are caught before they reach users
• Build and optimize CI/CD pipelines (GitHub Actions, ArgoCD) that cover not just code deployment but agent evaluation gates — no agent ships without passing its eval suite
• Automate Docker and package builds, security scanning, and agent integration tests as first-class pipeline steps
• Design self-healing CI patterns where agent-based automation can diagnose and fix common pipeline failures
• Build internal tools and developer self-service interfaces that let ML engineers and data scientists iterate on agents without platform team involvement
• Maintain a comprehensive view of how all platform components -> infrastructure, agent harnesses, evaluation pipelines, observability — work together
• Create architecture diagrams and drive long-term platform vision; own the "how does this scale to 10x" conversation
• Establish alerting (Grafana, PagerDuty) for both traditional platform health and agent-specific signals (error rates, tool call failures, eval score drift)
• Ensure all agent infrastructure adheres to security best practices: sandboxed execution, auditable traces, access controls on every tool
• Participate in security reviews; own compliance for agent workloads

Principal AI Engineer

• Partner closely with Marketing strategy, operations, and creative leads to surface highest-impact, repeatable workflows
• Design comprehensive end-to-end workflow architectures
• Architect and build scalable, reliable AI agents that execute full marketing workflows
• Design and implement distributed systems that securely integrate with Marketing’s existing tools
• Define and instrument success metrics for every agent
• Maintain and evolve a shared system context repository
• Communicate technical trade-offs and architectural decisions clearly to non-technical Marketing leadership

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• Champion Agile principles and practices across AI and data-focused teams, driving adoption and continuous improvement
• Coach product owners, engineers, and business stakeholders on Agile frameworks such as Scrum, Kanban, or SAFe
• Facilitate core Agile ceremonies (sprint planning, stand-ups, reviews, retrospectives) to ensure effective team execution
• Foster a culture of transparency, collaboration, and iterative delivery across cross-functional teams
• Partner with technical teams (data science, ML engineering) and business leaders to translate AI use cases into actionable backlogs and sprint goals
• Guide teams in delivering AI/ML solutions iteratively, accounting for experimentation, model training, and validation cycles
• Align delivery plans with business objectives, ensuring AI initiatives provide measurable value
• Support prioritization of work based on business impact, technical dependencies, and data readiness
• Enable collaboration across diverse teams, including data scientists, engineers, product managers, and domain experts
• Remove impediments and escalate risks impacting delivery timelines or outcomes
• Act as a liaison between technical and non-technical stakeholders to ensure shared understanding
• Define and track Agile and AI-specific performance metrics (e.g., velocity, cycle time, model performance indicators)
• Ensure AI delivery adheres to governance, compliance, and ethical standards (e.g., bias, data privacy)
• Facilitate retrospectives and apply learnings to improve delivery effectiveness and team maturity
• Promote data-driven decision-making and experimentation
• Support enterprise Agile transformation efforts, particularly within AI and digital initiatives
• Influence leaders and stakeholders to adopt Agile ways of working and AI-enabled delivery models
• Drive mindset shifts toward iterative development, experimentation, and continuous learning

AI Platform Agilist

• Build and nurture meaningful relationships with clients
• Utilize advanced analytical techniques to drive innovation
• Work with cross-functional teams to achieve project goals
• Uphold the firm's ethical standards and recommended practices

• Manage client accounts and secure the delivery of exceptional results
• Foster sturdy relationships with clients to understand their needs
• Analyze complex data sets to derive actionable insights
• Confirm adherence to quality standards and established practices in deliverables

AI Engineer Manager

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• analyze complex problems and provide strategic insights
• mentor and guide junior associates in their roles
• maintain rigorous standards of quality and compliance in deliverables
• navigate complex situations to achieve project goals
• support enterprise-scale transformation initiatives for clients

Senior Associate – AI Engineering

• work to design, deploy, and optimize Generative AI solutions
• build agentic workflows and RAG systems
• apply latest advancements in Foundation Models to create AI-native tools
• build and refine LLM-powered features and autonomous agents
• design and implement RAG pipelines to connect proprietary data with LLMs
• explore and eliminate GTM friction points by rapid prototyping AI solutions
• integrate AI workflows into modern sales stack (e.g., Salesforce, Gong)
• develop and maintain production-grade infrastructure for AI orchestration

AI Engineer – Go To Market

• Advise and make recommendations to senior IT and business leaders on areas for service simplification.
• Define and maintain system architecture and technology stack.
• Collaborate with product managers, designers, business intelligence engineers, and other stakeholders to make technical decisions, define technical requirements, and create development roadmaps in service of business goals.
• Conduct code reviews, provide constructive feedback, and promote continuous improvement in code quality and development processes.
• Mentor engineers and enforce best practices.
• Contribute hands-on across the stack.
• Collaborate with DevOps for CI/CD and cloud deployments.
• Participate in hiring and onboarding new team members.

• Define architecture patterns and technical standards for AI-enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Translate business and technology use cases into scalable solution architectures, including application design, data flows, integration patterns, model usage, security controls, and operational requirements.
• Partner with the Sr. Director, AI Platform and Engineering to shape platform architecture, technical roadmaps, reference implementations, and engineering playbooks.
• Provide hands-on architecture leadership in design reviews, technical decision-making, proof-of-concept evaluation, implementation planning, and production readiness.
• Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, cloud AI services, enterprise knowledge systems, and secure deployment practices.
• Design reusable platform patterns for model access, retrieval-augmented generation, vector databases, semantic search, embeddings, enterprise knowledge integration, prompt and response handling, and AI observability.
• Create architecture blueprints, technical standards, reusable components, templates, and implementation guidance that improve speed, consistency, quality, and reuse.

See all jobs on JobTailor

AI Engineer

Staff AI Engineer

AI Engineer, AI Analyst

GenAI Engineer – Associate

AI Engineer / AI Analyst

AI Engineer

Senior AI Engineer

Dynamics 365 Enterprise AI Architect

AI Engineer

Principal AI Engineer

Principal AI Engineer

QuantAI Engineer – Trading, Investment & Optimization

AI Platform Agilist

Senior AI Engineer

AI Engineer Manager

Senior Manager, AI Lead – Semantic Layer

Senior Associate – AI Engineering

AI Engineer – Go To Market

Principal AI Engineer

Principal AI Engineering Architect