ai solutions architect

International Swaps and Derivatives Association

<div><div>June 30, 2026
Job Title: Assistant Director, Artificial Intelligence
Reports to: Senior Director, Digital Transformation, Analytics
Location: London
</div><div>Job purpose 
This is a hands-on role. The successful candidate will architect and build production AI systems directly, while owning delivery, quality, evaluation, and partner management across the AI Solutions Lab portfolio. The role reports to the Senior Director of Digital Transformation and head of the AI Solutions Lab. 


Duties and responsibilities
<ul>
<li>Deliver the next phase of the ISDA AI Solutions Lab&rsquo;s buildout, helping move current AI initiatives from prototype/MVP stage toward robust, scalable and member-ready delivery. Architect and build the next phase of AI agents, external and internal.</li>
<li>Own the design and delivery of AI solutions that make ISDA standards and digital solutions more accessible, usable and AI-ready for members.</li>
<li>Help shape and scale the ISDA Knowledge Base and Assistant as the governed foundation for AI-enabled access to ISDA standards, taxonomies and related IP.</li>
<li>Oversee enhancement of priority AI capabilities including the CDM Coder, DRR Translator, Tracer, cross-jurisdiction analysis and AI legal opinions initiatives.</li>
<li>Help drive development and rollout of internal AI capabilities and agents aligned with the internal AI deployment roadmap.</li>
<li>Define product requirements, broader Agents orchestration, architecture direction, evaluation harnesses and quality standards and delivery priorities across the AI Solutions Lab portfolio.</li>
<li>Work closely with internal SMEs across CDM, DRR, legal, IT and product teams to ensure solutions are accurate, grounded and useful in real workflows.</li>
<li>Reach out to members for pulse checks and convert member and stakeholder feedback into roadmap decisions, product refinements and delivery choices to maximize adoption.</li>
<li>Help define practical connectivity and delivery models, including MCP servers and packaged AI Skills, so members can consume ISDA AI capabilities safely within their own environments.</li>
<li>Contribute to broader ISDA AI strategy development, including alignment between external member-facing AI solutions and internal AI deployment priorities.</li>
<li>Lead AI quality, guardrails and governance across the Lab&rsquo;s solutions, including evaluation harnesses, groundedness and traceability checks, observability and tracing, confidence frameworks, security and safe use boundaries, both internally and externally.</li>
<li>Own cost aware design of AI workloads, balancing performance and reliability against inference and infrastructure spend.</li>
<li>Manage relevant external partners and specialist vendors, ensuring work is aligned to ISDA&rsquo;s priorities, timelines, architecture and ownership model.</li>
<li>Support the Senior Director including for executive and board reporting by articulating progress, key risks, delivery needs and strategic value in a clear and concise way.</li>
<li>Help build a durable internal AI capability for ISDA that protects ISDA IP, accelerates adoption of ISDA standards and reinforces ISDA&rsquo;s strategic position in the market.</li></ul> 


Relevant Skills / Abilities
<ul>
<li>Strong senior-level experience (at least five years) leading the design and delivery of AI-enabled products or platforms from prototype through controlled rollout and production, with a demonstrable track record of shipping production AI systems that serve real users.</li>
<li>Hands-on engineering ability in Python (and ideally TypeScript/React for front end UI) and Java, and comfortable building production systems directly.</li>
<li>Deep understanding of LLMs, retrieval-augmented generation (including hybrid retrieval across semantic, graph and keyword search, with reranking and embedding-model selection), knowledge bases, knowledge graphs and ontologies, agentic workflows and skills, evaluation methods (eval harnesses, LLM-as-judge, groundedness and faithfulness metrics, tracing), and model risk controls.</li>
<li>Experience building secure, scalable and reliable AI solutions in enterprise environments, including governance, permissions, auditability and data protection including private retrieval architectures and ensuring proprietary data is not exposed to external model providers</li>
<li>Hands-on AWS experience (Bedrock, Neptune, OpenSearch, Lambda) is very important. Strong engineers able to ramp quickly will also be considered. Cost-aware design of inference and infrastructure spend is expected.</li>
<li>Proven ability to translate complex domain knowledge into usable AI products, ideally in financial markets, regulatory technology, legal tech, standards or data-intensive environments; or a demonstrated ability to ramp quickly on a complex domain.</li>
<li>Strong product and solution architecture skills, with the ability to define use cases, prioritize roadmap items and align technical delivery with strategic business outcomes.</li>
<li>Experience overseeing external vendors, technology partners and proof-of-concept work, while retaining internal control of direction, quality and IP.</li>
<li>Ability to work closely with subject matter experts to refine AI tools iteratively and turn specialist feedback into practical product improvements.</li>
<li>Strong grasp of structured data, taxonomies, ontology-style data models and mappings across complex standards or schemas.</li>
<li>Experience with user testing, controlled pilots, adoption planning, training and feedback loops for specialist user populations.</li>
<li>Strong judgment on AI risks, including hallucination, groundedness, security, explainability, confidence scoring and safe deployment boundaries.</li>
<li>Degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related discipline, or equivalent practical experience, is desirable.</li>
<li>Excellent communication skills, including the ability to understand member issues, to work across the organization and influence and align stakeholders, and to brief senior management and explain technical issues in concise business language.</li>
<li>Demonstrated ability to operate in a lean, hands-on environment, balancing strategy, execution and stakeholder engagement.</li></ul></div></div>

Assistant Director, Artificial Intelligence, London

Talan

<div>Company Description 
Talan Data x AI is a leading Data Management and Analytics consultancy, working closely with leading software vendors and top industry experts across a range of sectors, unlocking value and insight from their data. At Talan Data x AI, innovation is at the heart of our client offerings, and we help companies to further improve their efficiency with modern processes and technologies, such as Machine Learning (ML) and Artificial Intelligence (AI). 
Job Description 
As our AI practice scales, we are seeking an experienced AI Architect / Engineer to act as the senior technical authority shaping how we design and deliver next-generation AI solutions. This is a role for someone who has moved beyond building individual models to architecting entire systems: agentic platforms, LLM-powered applications, and the production infrastructure that makes them dependable at enterprise scale.
You will sit at the intersection of strategy and delivery, operating across multiple client engagements simultaneously. In the early phases you will shape solutions, validate technical approaches, and translate ambiguous business problems into robust, deployable architectures. As engagements mature, you will provide the technical oversight that keeps delivery teams aligned to the original design intent. Your influence will be felt in pre-sales conversations, solution blueprints, and the technical standards that define how our practice builds.
This is a hands on architectural role, not a purely advisory one. We expect you to remain close to the code, to prototype where it accelerates a decision, and to set a credible technical example for the engineers and data scientists you guide.
Required skills &amp; experience:<ul><li>
Lead end-to-end solution architecture for AI and LLM-based systems, spanning data, integration, infrastructure, and deployment.</li><li>
Design agentic architectures and multi-agent orchestration patterns, defining how LLMs, tools, APIs, and retrieval layers interact reliably.</li><li>
Shape early-stage engagements (scoping, solutioning, estimation, and pre-sales) turning client ambition into deliverable technical plans.</li><li>
Provide technical validation and quality oversight across several concurrent projects, mitigating risk before it reaches delivery.</li><li>
Define LLM integration and prompting strategies, including RAG, fine-tuning, evaluation, and guardrailing approaches.</li><li>
Establish MLOps and LLMOps practices that ensure solutions are observable, testable, and production ready.</li><li>
Set architectural standards, reusable patterns, and reference implementations that raise the quality bar across the practice.</li><li>
Partner with Responsible AI colleagues to embed safety, security, and governance into solution designs from the outset.</li></ul> 

Qualifications 
To qualify for this role, you must have:
<ul><li>Minimum 5 years in AI architecture or AI engineering, ideally 10+, with a track record of production deployments.</li><li>
Deep expertise in LLMs, agent frameworks (e.g. LangGraph, AutoGen, Semantic Kernel), API design, and orchestration.</li><li>
Strong cloud engineering experience.</li><li>
Proven MLOps / LLMOps capability &mdash; CI/CD for models, monitoring, evaluation, and cost optimisation.</li><li>
Hands-on software engineering background (Python and modern tooling) underpinning your architectural decisions.</li><li>
Comfort managing multiple engagements at once, with pragmatic judgement on trade-offs and delivery risk.</li></ul>
Ideally, you&rsquo;ll also have, but not essential:<ul><li>
Experience in consulting, professional services, or pre-sales environments.</li><li>
Exposure to enterprise-scale, regulated AI deployments (financial services, public sector, healthcare).</li><li>
Familiarity with vector databases, knowledge graphs, and advanced retrieval techniques.</li><li>
Relevant cloud or AI certifications.</li></ul>
You must be:<ul><li>
Willing to work on client sites, potentially for extended periods.</li><li>
Willing to travel for work purposes and be happy to stay away from home for extended periods.</li><li>
Eligible to work in the UK without restriction.</li></ul> 
#TalanUK #LI-HB1 
Additional Information 
Compensation &amp; Benefits Package:<ul><li>
BDP Plus &ndash; A reward programme whereby you accrue points to trade against a 3-month paid sabbatical or cash equivalent.</li><li>
25 days holiday + bank holidays.</li><li>
5 days holiday buy/sell option.</li><li>
Private medical insurance.</li><li>
Life cover.</li><li>
Cycle to work scheme.</li><li>
Eligibility for company pension scheme (5% employer contribution, salary sacrifice option).</li><li>
Employee assistance programme.</li><li>
Bespoke online learning via Udemy for Business.</li></ul>
Why join:
You will be a founding technical voice in a practice that is investing decisively in AI. The work is varied, intellectually demanding, and commercially significant, you will shape solutions that reach production, not slideware. You will have the autonomy to define how we build, the support of a collaborative multidisciplinary team, and a clear platform to grow into a practice-leading technical figure.</div>

AI Architect / Engineer (Agentic AI & LLM Architecture)

Mastercard

<div>Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we&rsquo;re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Senior Principal Agentic Platforms Architect
Overview
 
 
The AI Center of Excellence is seeking a Senior Principal Agentic Platforms Architect to lead the technical vision, architecture, and hands-on development of Mastercard's enterprise agentic AI platforms. This role demands deep, demonstrated expertise in building and shipping production-grade AI and agentic systems at enterprise scale, combined with the architectural rigor required to operate within one of the most highly regulated and security-conscious environments in the world. Reporting to senior leadership, you will own the E2E platform architecture for agentic AI systems, from runtime orchestration and governance infrastructure through to production deployment and operational lifecycle management. This is not a strategy-only role. You will write code, review code, and hold the team to the highest standards of engineering excellence. The right candidate has spent their career building AI systems that run in production, not prototypes, not proofs of concept, but systems that operate at scale, under load, and under scrutiny 
 
Role
 
 
<ul><li>Serve as the principal technical authority for agentic AI platform architecture, owning the design and evolution of runtime environments, governance layers, orchestration engines, and multi-tenancy infrastructure that support enterprise-grade AI agent deployment</li></ul> 
<ul><li>Lead hands-on architecture and development of agentic systems, including agent orchestration frameworks, tool and model integration layers, policy enforcement engines, guardrail systems, behavioral monitoring pipelines, and evaluation infrastructure</li></ul> 
<ul><li>Design and implement scalable, resilient platform architectures that support multi-agent coordination, A2A communication protocols, memory governance, and real-time observability across distributed deployments</li></ul> 
<ul><li>Drive the technical strategy for model integration and abstraction, enabling model-agnostic agent execution across multiple providers while enforcing cost optimization, routing intelligence, and governance controls at the platform level</li></ul> 
<ul><li>Architect and build API-first platform interfaces that enable both internal teams and external consumers to build, deploy, govern, and operate AI agents through programmatic, language-agnostic access patterns</li></ul> 
<ul><li>Define and enforce engineering standards, code quality practices, and architectural governance across the platform engineering team, ensuring every component meets production-grade requirements for security, resilience, performance, and compliance</li></ul> 
<ul><li>Provide deep technical leadership on agentic AI design patterns, including retrieval-augmented generation, tool calling via Model Context Protocol, multi-step orchestration, human-in-loop workflows, and autonomous decision-making architectures</li></ul> 
<ul><li>Act as a trusted technical advisor to senior leadership, translating complex platform architecture decisions into clear, business-oriented language that supports informed decision-making at the executive level</li></ul> 
<ul><li>Mentor and develop a high-performing engineering team, fostering a culture of technical excellence, intellectual honesty, rigorous code review, and continuous improvement</li></ul> 
<ul><li>Produce and maintain authoritative architectural documentation, including system design documents, architecture decision records, and integration specifications that meet Mastercard's engineering principles and security standards</li></ul> 
All About You
 
 
<ul><li>15+ years of hands-on experience in software engineering and AI engineering, with a significant and demonstrable portion spent designing, building, and shipping AI and machine learning systems to production at enterprise scale</li></ul> 
<ul><li>Proven track record of architecting and delivering agentic AI systems, autonomous agents, multi-agent orchestration platforms, or AI-powered automation systems that operate in production with real users, real data, and real consequences</li></ul> 
<ul><li>Deep hands-on coding proficiency. You write production code, you review production code, and you hold others to the same standard. This is a building role, not a diagram-drawing role</li></ul> 
<ul><li>Advanced expertise in Python and modern AI/ML frameworks, with hands-on experience across agentic toolkits such as LangChain, LangGraph, CrewAI, or equivalent orchestration frameworks</li></ul> 
<ul><li>Strong architectural experience with cloud-native platforms including AWS, Databricks, and Kubernetes, with demonstrated ability to design for high availability, fault tolerance, and horizontal scalability</li></ul> 
<ul><li>Deep understanding of AI governance, trust, and safety architectures, including guardrail systems, policy engines, behavioral analytics, evaluation frameworks, red teaming methodologies, and compliance mapping</li></ul> 
<ul><li>Experience designing and building multi-tenant platforms with fine-grained access control, workspace isolation, and enterprise-grade identity and authorization systems</li></ul> 
<ul><li>Proven ability to design API-first architectures, RESTful interfaces, and SDK abstractions that serve both internal platform teams and external consumers</li></ul> 
<ul><li>Strong understanding of data architectures including Delta Lake, data lakehouse patterns, vector databases and retrieval-augmented generation pipelines</li></ul> 
<ul><li>Demonstrated experience with tool calling architectures and model-agnostic integration patterns across multiple LLM providers</li></ul> 
<ul><li>Exceptional communicator who can articulate deeply technical architecture decisions to engineering teams and translate complex platform concepts into business language for non-technical stakeholders</li></ul> 
<ul><li>Known for building trust, holding high standards, and mentoring engineers. You make the people around you better. Motivated by hard problems, not titles. You bring intellectual curiosity, resilience, and relentless energy to complex technical challenges</li></ul> 
<ul><li>Bachelor's degree in Computer Science, Engineering, or a related field. Advanced degree preferred but not required if the experience speaks for itself</li></ul>
Corporate Security Responsibility 
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:<ul><li>
Abide by Mastercard&rsquo;s security policies and practices;</li><li>
Ensure the confidentiality and integrity of the information being accessed;</li><li>
Report any suspected information security violation or breach, and</li><li>
Complete all periodic mandatory security trainings in accordance with Mastercard&rsquo;s guidelines.</li></ul></div>

Senior Principal Agentic Platforms Architect

Lorien

<div>
 
 AI Solutions Engineer / Applied AI Engineer
 
 
 Location: London (Hybrid) 3 days onsite
 
 
 Perm
 
 
 Cheapside, London EC2V 6EE
 
 
 
 
 
 Criteria:
 
 
 &bull; AI-Native Engineering Practice
 
 
 &bull; Software Engineering Experience
 
 
 &bull; Databricks and Python Proficiency
 
 
 &bull; Functional Empathy and Stakeholder Engagement
 
 
 &bull; Solution Ownership and Quality
 
 
 
 
 
 We&rsquo;re looking for engineers who are not only deeply technical in AI but also excellent communicators and stakeholder partners.
 
 
 
 
 
 Some engineers love technology for technology&rsquo;s sake. This role is different. At HeadFirst x Impellam, we&rsquo;re looking for someone who enjoys figuring things out, understands how organizations work, and gets a kick out of building solutions that genuinely improve the day-to-day reality of teams across the business.
 
 
 
 
 
 As an Applied AI Engineer, you work directly with departments such as Finance, HR, Sales, and Marketing to streamline processes, eliminate friction, and help people work smarter through AI, automation, and lightweight internal applications. You aren&rsquo;t expected to be an expert in every framework, platform, or tool from day one. What matters more is how you think, how quickly you learn, and how effectively you turn ambiguity into practical results.
 
 
 This is a high-profile role that makes a real difference to the business. You&rsquo;ll spend just as much time understanding workflows, challenging assumptions, and shaping solutions as you do building them. The people who thrive here are curious, pragmatic, and comfortable working closely with stakeholders rather than hiding behind tickets or isolated engineering backlogs.
 
 
 Your impact
 
 
 
 
 
 As an Applied AI Engineer, you&rsquo;ll serve as a consultant, a builder, and a problem solver all in one. You&rsquo;ll work closely with business teams to understand how work actually gets done. Sometimes the challenge is operational inefficiency. Sometimes it&rsquo;s fragmented tools, repetitive manual work, or slow decision-making caused by disconnected systems and unclear information flows.
 
 
 Your role is to identify where AI, automation, or lightweight applications can deliver immediate value, and then help bring that value to life quickly and pragmatically. Rather than spending months designing perfect systems, you focus on delivering usable solutions quickly, gathering feedback early, and improving continuously. This could involve building an internal workflow tool, creating an AI-powered assistant for operational teams, or streamlining reporting and approvals through automation.
 
 
 
 
 
 You work within a modern Databricks and AI-enabled ecosystem, but technology is never the end goal. The real objective is to help teams operate more effectively, make better decisions, and eliminate operational bottlenecks. AI plays a central role in how you work. AI coding assistants and agent-based workflows help accelerate development and experimentation, allowing you to move quickly while still maintaining control over quality, usability, and adoption.
 
 
 At the same time, you work closely with the Platform, Data, and Engineering teams to ensure that the solutions you deliver fit within a scalable and secure organizational environment.
 
 
 
 
 
 What you will do
 
 
 &bull; Work closely with business stakeholders to understand operational challenges, workflows, and inefficiencies;
 
 
 &bull; Translate ideas and business needs into practical tools, automations, and AI-powered solutions;
 
 
 &bull; Build lightweight internal applications that boost productivity and simplify daily work;
 
 
 &bull; Use AI coding assistants and AI-native workflows to speed up delivery and experimentation;
 
 
 &bull; Continuously refine solutions based on real user feedback and adoption;
 
 
 &bull; Support teams in improving processes rather than simply digitizing existing inefficiencies;
 
 
 &bull; Collaborate with Finance, HR, Commercial, and leadership teams on business-critical initiatives;
 
 
 &bull; Balance speed and quality by making pragmatic engineering decisions that maintain momentum;
 
 
 &bull; Introduce smarter workflows through automation, AI agents, and integrated internal tools;
 
 
 &bull; Help shape how AI is adopted responsibly and practically throughout the organization;
 
 
 &bull; Share ideas, best practices, and suggestions for improvement that help teams move faster and work more effectively.
 
 
 
 
 
 Who you are
 
 
 You&rsquo;re someone who enjoys solving problems in complex, real-world environments. You likely combine technical curiosity with strong communication skills and a genuine interest in how businesses operate. You don&rsquo;t need perfect requirements or fully defined roadmaps to get started. Instead, you ask questions, explore possibilities, and move toward solutions quickly. You&rsquo;re energized by learning new things and comfortable adapting as technology evolves. AI doesn&rsquo;t intimidate you. You see it as a practical tool that helps you think faster, build faster, and create more impact.
 
 
 You enjoy collaborating and are comfortable working closely with non-technical stakeholders. You know how to explain ideas clearly, challenge assumptions constructively, and maintain momentum without overcomplicating things. Most importantly, you care about results. You enjoy delivering useful solutions, seeing people adopt them, and improving them over time based on what works in practice.
 
 
 
 
 
 Why this role stands out
 
 
 This is not a traditional engineering role focused solely on technical implementation. You will work closely with decision-makers, influence how teams operate, and help shape the practical implementation of AI across an international organization. The role combines engineering, consulting, problem-solving, and business collaboration in a way that offers broad exposure and a tangible impact.
 
 
 If you enjoy fast-paced environments, practical innovation, and building solutions that people actually use, this role offers the opportunity to have a direct impact on how modern AI capabilities create value within a global business.
 
</div>

AI Solutions Engineer / Applied AI Engineer

Google

<div><h3 class="jobSectionHeader">Minimum qualifications:</h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience. 
</li>
<li>2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree. 
</li>
<li>1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).</li>
<li>Experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).</li>
<li>Ability to communicate in French and German fluently to support client relationship management in this region.</li></ul> 
<h3 class="jobSectionHeader">
Preferred qualifications:</h3><ul>
<li>Master&rsquo;s or PhD in AI, Computer Science, or a related technical field.</li>
<li>Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google&rsquo;s ADK) and complex patterns like ReAct, self-reflection, and hierarchical delegation.</li>
<li>Knowledge of &quot;LLM-native&quot; metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.</li>
<li>Ability to implement secure agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.</li>
</ul><div><h2 class="jobSectionHeader">About the job</h2>
Google Cloud&rsquo;s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren&rsquo;t just building technology; you&rsquo;re shaping the frontier of enterprise and driving the evolution of advanced models.
Leading with the future, the AI go-to-market (GTM) team are trusted AI advisors, scaling across Google to deliver transformative customer experiences that shape the next generation of their business. Partnering to provide deep AI expertise, thought leadership, and industry best practices, we revolutionize customer outcomes.<div></div>
It's an exciting time to join Google Cloud&rsquo;s Go-To-Market team, leading the AI revolution for businesses worldwide. You&rsquo;ll excel by leveraging Google's brand credibility&mdash;a legacy built on inventing foundational technologies and proven at scale. We&rsquo;ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We&rsquo;re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era&mdash;the market is yours.</div><div><h2 class="jobSectionHeader">
Responsibilities</h2><ul>
<li>Serve as the lead developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol (MCP) servers) that drive measurable return on investment.</li>
<li>Architect and code the &quot;connective tissue&quot; between Google&rsquo;s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.</li>
<li>Build high-performance evaluation (Eval) pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency. 
</li>
<li>Identify repeatable field patterns and technical &quot;friction points&quot; in Google&rsquo;s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.</li>
<li>Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Forward Deployed Engineer III, Generative AI (French, German)

<div><h3 class="jobSectionHeader">Minimum qualifications:</h3><ul>
<li>Bachelor's degree or equivalent practical experience.</li>
<li>6 years of experience with cloud-native architecture in technical delivery, customer engineering, or enterprise architecture and in a customer-facing or enterprise support role.</li>
<li>Experience in technical deployment planning, architecture orchestration, or technical delivery management.</li>
<li>Experience with enterprise integrations (APIs, ECMs, identity), Cloud infrastructure, and AI/ML model deployments.</li>
<li>Experience with programming languages, API integration, debugging, or enterprise systems design.</li>
<li>Experience leading technical delivery strategies, debugging systems, and interfacing with product/engineering organizations.</li></ul> 
<h3 class="jobSectionHeader">
Preferred qualifications:</h3><ul>
<li>L300/L400 level technical proficiency or relevant AI/ML and Cloud architecture certifications.</li>
<li>Experience guiding customers through the architectural and organizational transformations needed to deploy production-grade AI models or agents in enterprise environments.</li>
<li>Technical experience in one or more of the following: Cloud AI solutions, Enterprise Data Management, Data Analytics, Infrastructure Modernization, Security, or Cloud Networking.</li>
<li>Ability to dive deep into novel technical problems, decipher extreme ambiguity, diagnose bugs, and emerge with credible architectural solution.</li>
<li>Proven track record interfacing directly with core product and engineering organizations to escalate product bugs, advocate for feature gaps, and unblock systemic deployments.</li>
</ul><div><h2 class="jobSectionHeader">About the job</h2>
The Google Cloud Platform team helps customers transform and build what's next for their business &mdash; all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers &mdash; developers, small and large businesses, educational institutions and government agencies &mdash; see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As an AI Outcome Customer Engineer (AI OCE), you will drive initial and ongoing revenue ramp for our enterprise customers, clearing complex blockers and ensuring they realize maximum business value from their Cloud AI investments faster. You will manage and execute the technical deployment plan, transitioning scoped license agreements into robust production deployments by providing authoritative technical leadership to customers and partners. 
You will have a direct impact on the velocity and incremental consumption of customer agreements, leading to accelerated value realization, higher adoption, and future expansion opportunities. You will blend sales acumen, enterprise architecture knowledge, technical engagement, and technical delivery management to prove and scale the value of Google's premier Cloud AI solutions.
Google Cloud accelerates every organization&rsquo;s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google&rsquo;s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.</div><div><h2 class="jobSectionHeader">
Responsibilities</h2><ul>
<li>Develop and orchestrate a structured, end-to-end technical deployment plan across customer and partner teams. Lead upfront architectural validation to ensure integration readiness before execution begins.</li>
<li>Dive into complex technical workflows to diagnose, debug, and resolve implementation roadblocks. Act as a technical bridge to core product and support engineering teams to eliminate systemic friction and accelerate customer delivery.</li>
<li>Drive and track the progress of the initial and ongoing ramp of Cloud AI license agreements, guiding enterprise workloads from agreement to active consumption as rapidly and securely as possible.</li>
<li>Partner collaboratively with Account an CEs team during the technical evaluation phase of key deals to ensure proposed Cloud AI solutions are architected for viable, scalable long-term adoption.</li>
<li>Drive sustainable, secure Cloud AI product usage to help customers continuously realize quantifiable business outcomes and secure future contract renewals.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

AI Outcome Customer Engineer, Cloud AI Tech GTM

Accenture

<div>YOU ARE
As an experienced and senior AI/LLM Architect, you will play a pivotal in designing and delivering end-to-end AI platform architectures that power the modern, reinvented enterprise. Operating at the intersection of business and engineering, you will own the technical design of advanced AI systems &mdash; spanning classical machine learning, generative AI, and agentic systems &mdash; ensuring they are purposefully architected to meet client business objectives and enterprise-grade standards.
Within this scope, you will take deep ownership of one or more critical architecture domains &mdash; such as agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, or model platforms and inference &mdash; serving as the lead authority in your domain across client engagements. You will develop and maintain specialized expertise in the technologies, patterns, and emerging practices within your domain, bringing that depth to bear in shaping architecture decisions, accelerating delivery, and building reusable assets that extend across the practice. You will evaluate, select, and apply the right design patterns, technical frameworks, and tools within your domain and across the broader AI architecture &mdash; ensuring cohesion across the full system. This includes architecting AI agents encompassing multi-agent orchestration, tool use, skills use, and memory systems, as well as the integration of fine-tuned foundation models and classical ML models into scalable, production-ready platforms.
A critical dimension of this is owning the design of a comprehensive AI context layer &mdash; drawing on enterprise knowledge bases, structured and unstructured data sources, and domain-specific content &mdash; to ground AI systems in the realities of each client's business and ensure outputs are accurate, trustworthy, and impactful. As the technical authority on your domains, you will lead architecture decisions and be accountable for ensuring systems meet rigorous non-functional requirements across security, observability, governance, performance, and scalability. You will produce and own the architecture artifacts that shape delivery &mdash; including architecture decision records (ADRs), component and data flow diagrams, and integration specifications &mdash; and provide the technical leadership that enables cross-functional teams of data engineers, ML engineers, and application developers to execute with clarity and confidence.
Your contributions will be instrumental in shaping how clients adopt and scale AI, pushing the boundaries of what these systems can achieve and delivering measurable, lasting business value.
THE WORK<ul><li>
Translate business strategy into a technical vision by defining the non-functional requirements (NFRs) necessary to meet operational goals for performance, reliability, and cost.</li><li>
Lead stakeholder workshops to align on technical feasibility, define project scope, and manage expectations with clients and leadership.</li><li>
Drive the technology selection process, evaluating build-vs-buy decisions for AI platforms (e.g., Arize, LangSmith) and foundational models.</li><li>
Architect model- and tool-agnostic multi-agent systems governed by an MCP Control Plane.</li><li>
Design and implement the Agent Registry as the mandatory system of record and the AI Gateway for runtime policy enforcement.</li><li>
Design and implement a certification gate to ensure no uncertified agents enter production, validating identity, policies, and evaluation metrics.</li><li>
Design, implement, and abstract core agent services, including a first-class abstracted memory service with semantic, episodic, and procedural endpoints.</li><li>
Architect the end-to-end data pipeline for AI systems, including data ingestion, preprocessing, and synchronization for fine-tuning and RAG.</li><li>
Design and implement the context layer&mdash;spanning knowledge graphs, vector search, and semantic retrieval&mdash;to create reliable, grounded RAG pipelines.</li><li>
Architect foundation model adaptation strategies, including dynamic, cost-and-performance-aware model routing and selection.</li><li>
Design, implement, and prototype high-throughput, low-latency inferencing solutions using techniques like response caching and request batching.</li><li>
Define security, governance, and observability as centrally-enforced, by-design controls for all AI systems.</li><li>
Architect a robust, defense-in-depth security framework, including per-agent identity with IAM/IAP binding and layered guardrails.</li><li>
Design and implement FinOps controls enforced at the AI Gateway, including token budgets, cost-center labeling, and threshold alerts.</li><li>
Establish the framework for comprehensive system evaluation, adopting productized tools and instrumenting observability with OTel</li><li>
Define and maintain the enterprise-wide AI reference architecture, reusable design patterns, and a library of approved software components.</li><li>
Independently design, implement, build, and deliver proof-of-concept prototypes and foundational software components to validate architectural decisions.</li><li>
Produce and own authoritative architecture artifacts, including blueprints, sequence diagrams, design specifications, and Architectural Decision Records (ADRs).</li><li>
Mentor and guide cross-functional engineering teams (data, ML, application) on architectural best practices and design patterns.</li><li>
Continuously research and integrate emerging AI patterns, frameworks, and technologies to maintain a forward-looking architecture.</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree or equivalent</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Experience in designing &amp; deploying enterprise grade advanced AI solutions using agentic, generative and classical AI/ML using at least one cloud vendor.</li><li>
Experience in the Agentic, LLM and Generative AI space.</li><li>
Coding experience using Python</li><li>
Experience architecting and operationalizing LLM driven application architecture patterns.</li><li>
Expertise in coding engineering, machine learning, deep learning and NLP solutions and applications.</li><li>
Experience as a machine learning architect in the industry designing big data, machine learning. large scale analytical engineering solutions.</li></ul></div>

AI LLM Technology Architecture Manager

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