Software Product Architect

Product Architect specializing in AI/ML, responsible for infusing AI/ML and GenAI into state-of-the-art products. This role involves hands-on architecture, technical leadership, and mentoring, with a focus on delivering outcome-driven, customer-centric engineering solutions. Requires deep expertise in modern software engineering, deep learning, Agentic AI, MLOps/AgentOps, and DevSecOps, with a collaborative approach to cross-functional teams.

What you'd actually do

  1. 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.
  2. 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.
  3. Develop lean engineering solutions through rapid, inexpensive experimentation to solve customer needs.
  4. Work collaboratively with empowered, cross-functional teams including product management, experience, delivery, infrastructure, and security.
  5. Translate business/user needs into technical requirements, designs, and robust data processing pipelines.

Skills

Required

  • Software architecture
  • AI/ML
  • GenAI
  • Data science
  • Modern frameworks
  • Programming languages
  • Engineering design
  • Technical feasibility
  • Customer needs analysis
  • Cross-functional collaboration
  • Product management
  • Experience design
  • Delivery management
  • Infrastructure knowledge
  • Security principles
  • Deep learning
  • Agentic AI
  • OOD/OOP
  • Agile methodologies
  • MLOps/AgentOps
  • DevSecOps
  • Continuous Integration/Continuous Deployment
  • Blue-Green deployment
  • Canary deployment
  • A/B testing
  • Product development
  • Domain expertise
  • Technical requirements translation
  • Data processing pipelines

What the JD emphasized

  • AI/ML and GenAI
  • end-to-end AI/ML solutions
  • Agentic AI solutions
  • MLOps/AgentOps

Other signals

  • infusing AI/ML and GenAI to build state of the art products
  • delivering solutions that delight customers and users
  • driving tangible value for Deloitte's business investments
  • leveraging extensive engineering and AI/ML craftsmanship
  • advanced proficiency across multiple programming languages, data science, and modern frameworks
  • exemplary track record in delivering high-quality, outcome-focused solutions
  • role model and engineering mentor
  • design, develop, and deploy advanced software solutions
  • Outcome-Driven Accountability
  • customer and business outcomes
  • engineering solutions that solve complex problems with valuable outcomes
  • high-quality, lean designs and implementations
  • Technical Leadership and Advocacy
  • technical advocate for products
  • architectural integrity, feasibility, and alignment with business and customer goals
  • NFRs, and applicable architecture and engineering standards
  • product architecture blueprints
  • high-level architecture designs
  • development/implementation of end-to-end AI/ML solutions
  • integration architecture into the technical landscape and technology stack
  • Engineering Craftsmanship
  • individual contributor
  • engineering designs and technical feasibility of solutions
  • 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
  • helping drive engineering quality
  • self-driven to learn new technologies
  • experiment with engineers
  • inspire the team to learn and drive application of those new technologies
  • Customer-Centric Engineering
  • 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
  • mindset that favors action and evidence over extensive planning
  • 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
  • deep expertise in modern software engineering practices and principles
  • deep learning and Agentic AI solutions
  • 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
  • Role-Model
  • leveraging these techniques to optimize solutioning and product delivery
  • ensuring high-quality outcomes with minimal waste
  • proficiency in product development, from conceptualization and design to implementation and scaling
  • 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