Product Architect (AI/ML) Role Overview: As a Product Architect specializing in AI/ML , you will actively engage in your software architecture craft, taking a hands-on approach to multiple high-visibility projects, infusing AI/ML and GenAI to build state of the art products. Your expertise will be pivotal in delivering solutions that delight customers and users, while also driving tangible value for Deloitte's business investments. You will leverage your extensive engineering and AI/ML craftsmanship and advanced proficiency across multiple programming languages, data science, and modern frameworks, consistently demonstrating your exemplary track record in delivering high-quality, outcome-focused solutions. The ideal candidate will be a role model and engineering mentor, collaborating with cross-functional teams to design, develop, and deploy advanced software solutions. Work You'll do: Outcome-Driven Accountability: Embrace and drive a culture of accountability for customer and business outcomes. Develop engineering solutions that solve complex problems with valuable outcomes, ensuring high-quality, lean designs and implementations. Technical Leadership and Advocacy: Serve as the technical advocate for products, ensuring architectural integrity, feasibility, and alignment with business and customer goals, NFRs, and applicable architecture and engineering standards-being responsible for product architecture blueprints, high-level architecture designs (e.g., 4+1 model or relevant others), development/implementation of end-to-end AI/ML solutions, and integration architecture into the technical landscape and technology stack. Engineering Craftsmanship: Possess passion and experience as an individual contributor, responsible for the engineering designs and technical feasibility of solutions, being hands-on with design, configuration and code part of the time, contributing to team velocity. Actively get engaged with engineers to ensure architecture is understood and can be implemented, working with them closely during sprints, helping resolve any technical issues through to production operations: reviewing code, actively driving technology debt reduction, and helping drive engineering quality. Be self-driven to learn new technologies, experiment with engineers, and inspire the team to learn and drive application of those new technologies. Customer-Centric Engineering: Develop lean engineering solutions through rapid, inexpensive experimentation to solve customer needs. Engage with customers and product teams to deliver the right architectural solution and machine learning models for the product in the right way at the right time. Incremental and Iterative Delivery: Exhibit a mindset that favors action and evidence over extensive planning. Utilize a leaning-forward approach to navigate complexity and uncertainty, delivering lean, supportable, and maintainable solutions. Cross-Functional Collaboration and Integration: Work collaboratively with empowered, cross-functional teams including product management, experience, delivery, infrastructure, and security. Integrate diverse perspectives to make well-informed decisions that balance feasibility, viability, usability, and value. Foster a collaborative environment that enhances team synergy and innovation. Advanced Technical Proficiency: Possess deep expertise in modern software engineering practices and principles, deep learning and Agentic AI solutions, including OOD/OOP, Agile methodologies, MLOps/AgentOps, DevSecOps, Continuous Integration/Continuous Deployment, deployment techniques like Blue-Green, Canary to minimize down-time and enable A/B testing approaches. Act as a Role-Model, leveraging these techniques to optimize solutioning and product delivery, ensuring high-quality outcomes with minimal waste. Demonstrate proficiency in product development, from conceptualization and design to implementation and scaling, with a focus on continuous improvement and learning. Domain Expertise: Quickly acquire domain-specific knowledge relevant to the business or product. Translate business/user needs into technical requirements, designs, and robust data processing pipelines. Navigate various enterprise functions such as business and enabling areas as well as product, experience, delivery, infrastructure, and security to drive product value and feasibility as well as alignment with organizational goals. Effective Communication and Influence: Exhibit exceptional communication skills, capable of articulating complex technical concepts clearly and compellingly. Inspire and influence stakeholders at all levels through well-structured arguments and trade-offs supported by evidence, evaluations, and research. Create coherent narratives that align technical solutions with business objectives. Engagement and Collaborative Co-Creation: Engage and collaborate with stakeholders at all organizational levels, from team members to senior executives. Build and maintain constructive relationships, fostering a culture of co-creation and shared momentum towards achieving product goals. Align diverse perspectives and drive consensus to create feasible solutions. The team: US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost-effective model that focuses on value/outcomes that leverages a progressive and responsive talent structure. As Deloitte's primary internal development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations with proven bottom-line results and outcomes. It helps power Deloitte's success. It is the engine that drives Deloitte, serving many of the world's largest, most respected companies. We develop and deploy cutting-edge internal and go-to-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence. Qualifications: Required: \\+ A bachelor's degree in computer science, software engineering, or a related discipline. An advanced degree (e.g., MS) is preferred but not required. Experience is the most relevant factor. \\+ Excellent software engineering and product architecture/design foundation with deep understanding of Business Context Diagrams (BCD), sequence/activity/state/ER/DFD diagrams, OOP/OOD, data-structures, algorithms, code instrumentations, etc. \\+ Extensive knowledge of AI/ML, deep learning, NLP, NLU, NLG with implementing the same in production. \\+ Understanding of supervised and unsupervised analytic modeling techniques such as linear and logistic regression, support vector machines, decision trees / random forests, Naïve-Bayesian, neural networks, association rules, text mining, and k-nearest neighbors among other clustering models. \\+ 10+ years proven track record of leading and delivering large-scale machine learning projects, including production model deployment and monitoring, data quality framework implementation, and experience with big data to create insights through predictive and prescriptive analytic models. \\+ 5+ years of hands-on experience in advanced Python (NumPy, Pandas, scikit-learn), SQL, PySpark on cloud hyper-scalers like Azure, AWS, and GCP. \\+ 2+ years of experience with Prompt Engineering--integrating GPT and other GenAI technologies into existing business processes to enhance decision-making and automate tasks. Experience with large language models such as GPT, BERT, Llama, as well as fine-tuning methodologies. \\+ Deep understanding of methodologies & tools like, XP, Lean, SAFe, DevSecOps, SRE, ADO, GitHub, SonarQube, etc. to deliver high quality products rapidly. \\+ Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care. \\+ Ability to travel 0-10%, on average, based on the work you do and the customers you serve. \\+ Limited immigration sponsorship may be available. The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $113,100 to $232,300. You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance. EA\\_ExpHire EA\\_ITS\\_ExpHire PXE\\_JOBS All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.

The role requires a bachelor's degree in computer science, software engineering, or a related discipline, though an advanced degree is preferred but not required, with experience being the most relevant factor. A strong foundation in software engineering and product architecture/design is necessary, including understanding of Business Context Diagrams, sequence/activity/state/ER/DFD diagrams, OOP/OOD, data structures, algorithms, and code instrumentation. Extensive knowledge of AI/ML, deep learning, NLP, NLU, and NLG with production implementation experience is essential. The position also requires understanding of supervised and unsupervised analytic modeling techniques and a proven track record of leading and delivering large-scale machine learning projects, including production model deployment and monitoring.