Sr Solution Architect, Google Tech

Job Title: Senior Manager: Solution Architect, Google
LOCATION: Bangalore

ABOUT UNILEVER:

Be part of the world’s most successful, purpose-led business. Work with brands that are well loved around the world, that improve the lives of our consumers and the communities around us. We promote innovation, big and small, to make our business win and grow; and we believe in business as a force for good. Unleash your curiosity, challenge ideas and disrupt processes; use your energy to make this happen. Our brilliant business leaders and colleagues provide mentorship and inspiration, so you can be at your best. Every day, nine out of ten Indian households use our products to feel good, look good and get more out of life – giving us a unique opportunity to build a brighter future. 

Every individual here can bring their purpose to life through their work. Join us and you’ll be surrounded by inspiring leaders and supportive peers. Among them, you’ll channel your purpose, bring fresh ideas to the table, and simply be you. As you work to make a real impact on the business and the world, we’ll work to help you become a better you. 

ABOUT GDT:

GDT is the global digital and technology engine of Unilever offering business services, technology, and enterprise solutions. GDT serves over 190 locations and through a network of specialized service lines and partners delivers insights and innovations, user experiences and end-to-end seamless delivery making Unilever Purpose Led and Future Fit. 

MAIN JOB PURPOSE:
This role focuses on executing, architecting, and scaling autonomous, agentic workflows for Unilever’s dynamic business landscape, supporting digital transformation initiatives across multiple areas like Supply Chain, Logistics, Customer Operations, Marketing, etc. This role will be a pivotal link between product teams who would detail business needs and technical execution, leading the design and delivery of transformative, AI-driven solutions. This role would be required to architect systems where AI agents autonomously manage complex workflows, interact with enterprise systems, and adapt to dynamic environments, driving significant operational efficiency and innovation. The position requires deep technical contribution, strong solution architecture grounding, and proficiency in Google Cloud's Gen AI stack (Vertex AI, Gemini, Agent Builder) at scale.

Key Responsibilities

I. Technical Execution & Solution Architecture
• Gen AI/Agentic Design: Lead the technical design and support the architecture of HyperAutomation solutions, specifically focusing on the build and scaling of Autonomous Agents, Agentic AI workflows, RAG systems, and multi-agent orchestration on the Google Cloud Platform (GCP) and strong understanding of Databricks agents
• Framework Implementation: Champion the adoption of technical standards, design patterns, and engineering best practices (e.g., A2A, MCP) across the team to ensure development of reusable AI agents that are scalable and maintainable.
• System Integration: Execute and document the technical integration of Gen AI solutions with core enterprise systems using modern API, event-driven (e.g., Pub/Sub), and microservices architectures.
• Governance & Safety: Implement "Human-in-the-Loop" (HITL) patterns, security guardrails, and audit trails to ensure autonomous actions align with ethical and regulatory standards.
• Code Quality & Review: Act as a key technical resource, conducting rigorous code reviews, debugging complex issues, and ensuring adherence to high-quality code standards for critical components.
• Innovation Competencies: Explore and deliver PoCs with new and relevant new technologies along with key use cases

II. Team & Project Coordination
• Team Support & Mentorship: Provide guidance, technical support, and mentorship to Software Engineers and Automation Developers, assisting in their professional development and growth.
• Delivery Execution: Support in managing the execution of the HAP team's roadmap, tracking progress against milestones, and ensuring timely delivery of sprint goals and features.
• Process Improvement: Ensure adherence to Unilever's software development lifecycle (SDLC), ensuring efficiency, quality control, and successful adoption of Agile/Scrum methodologies.

III. Generative AI & Google Cloud Focus
• LLMOps Contribution: Assist in the implementation and operation of GenAIOps (MLOps for Gen AI) pipelines using Vertex AI Pipelines, Databricks Genie, focusing on automated testing, monitoring, and versioning of AI models and prompts.
• Cloud Optimization Analysis: Conduct detailed analysis of cloud resource usage for AI workloads on GCP, identifying opportunities for cost optimization and operational efficiency.
• Evaluation & Validation: Implement and run model evaluation (EVAL) frameworks to validate agent performance, monitor accuracy metrics, and ensure adherence to safety and quality guardrails.
• Google Stack Utilization: Leverage and apply Google AI tools, including Gemini models, Vertex AI, Agent Builder, and Dialogflow CX, to build effective hyperautomation solutions.

IV. Stakeholder Communication & Analysis
• Requirement Translation: Work closely with Product Managers and business analysts to break down complex business requirements into clear, executable technical tasks and user stories.
• Architecting Solution: Drive the most optimal (performance and cost) solution design and be able to translate it into business benefits during build / run phases, articulate the advantages to stakeholders and during architecture board discussions.
• Cross-Functional Collaboration: Facilitate communication and synchronization between the engineering team and dependent teams (Enterprise Architecture, Data Science, Infrastructure) to resolve integration issues.
• Technical Documentation: Produce high-quality technical documentation for solution designs, API specifications, and operational playbooks to enable smooth platform adoption and support.

KEY REQUIREMENTS
This role is for a highly technical Google Cloud Gen AI/Hyperautomation expert who will design, build, and scale autonomous, agentic AI workflows for Unilever's global business.

Key Focus Areas:
1. Technical Execution Architecting and implementing end-to-end Agentic AI solutions, RAG pipelines, and multi-agent orchestration frameworks on GCP — leveraging Vertex AI, Gemini, and Agent Builder to deliver production-grade, scalable enterprise systems.
2. Model Strategy Maintaining deep, hands-on understanding of the Gemini model family — including multimodal reasoning, large context window management, and model selection trade-offs to match capability to business use case.
3. Observability & Agent Monitoring Designing and implementing AI-driven monitoring frameworks using Google Cloud Observability — enabling real-time tracking of agent behavior, drift detection, anomaly alerting, and explainability across deployed AI systems.
4. GenAIOps & LLMOps Building and operationalizing LLMOps pipelines on Vertex AI — covering automated testing, prompt versioning, model lifecycle management, and continuous evaluation to ensure reliability at enterprise scale.
5. Integration & Engineering Quality Enforcing high engineering standards across AI system integrations — conducting rigorous architecture and code reviews, and driving seamless connectivity with enterprise systems via modern REST/GraphQL APIs and event-driven architectures such as Pub/Sub and Eventarc.
6. Cost Optimization Defining and executing FinOps strategies for AI and Generative AI workloads — including compute rightsizing, model serving efficiency, token cost management, and scalable architecture patterns that balance performance with fiscal responsibility.
7. Technical Strategy & Mentorship Partnering with business and product leaders to translate complex enterprise requirements into executable technical roadmaps — while providing hands-on mentorship, unblocking delivery teams, and elevating technical decision-making across the group.
8. Team Management Leading and scaling a blended team of internal engineers and vendor resources — setting clear expectations, managing performance, fostering accountability, and building a high-trust delivery culture that consistently ships quality outcomes.
9. Enterprise Architecture & Governance Defining AI solution blueprints, reference architectures, and guardrails that align with enterprise security, compliance (data residency, GDPR), and GCP landing zone standards. Includes IAM, VPC-SC, and responsible AI policy enforcement.
10. AI Product & Roadmap Co-ownership Collaborating with product managers and business stakeholders to shape the AI innovation roadmap — translating market signals and emerging GCP capabilities into prioritized, deliverable initiatives with clear OKRs.
11. Data Strategy & AI Readiness Ensuring the data foundations (BigQuery, Dataplex, Vertex Feature Store) are fit-for-purpose for AI workloads — covering data quality, lineage, governance, and real-time vs. batch pipeline design for model training and inference.
12. Developer Experience & Platform Engineering Building internal AI platforms, toolchains, and reusable accelerators (prompt libraries, agent templates) that enable developer teams to ship faster without reinventing the wheel on every project.
13. Vendor & Partner Ecosystem Management Managing relationships with Google, other product partners, and vendors — evaluating third-party AI tools, negotiating technical scope with vendors, and ensuring partner-delivered work meets architectural standards.
14. Innovation Pipeline & Proof-of-Value Running structured discovery sprints, COPs, hackathons, and PoC/PoV cycles to evaluate emerging AI capabilities — with clear frameworks to graduate experiments into production-grade solutions.
15. Risk, Security & Responsible AI Owning AI risk management: model bias evaluation, hallucination mitigation strategies, adversarial prompt testing, audit logging for agent decisions, and aligning with the enterprise's responsible AI framework.
16. Stakeholder Communication & Executive Engagement Translating technical architecture into business narratives for C-suite and board-level audiences — including investment cases, risk trade-offs, and milestone reporting on AI programs.
17. Talent Development & Capability Building Beyond team management — defining skill matrices, running internal upskilling programs (GCP certifications, AI/ML foundations), and building a hiring pipeline that attracts strong AI engineering talent.

LEADERSHIP SKILLS

• CARE DEEPLY: We care about how consumers experience our brands, the growth and development of our people, and their impact on the planet. We emphasize the importance of performance and care, moving from ambiguity about success to fairness and transparency. 

• FOCUS ON WHAT COUNTS: We prioritize what truly matters, setting clear and stretching goals. We aim to shift from having everything as a priority to focusing on fewer, bigger things that are delivered to conclusion and are being rewarded. 

• STAY THREE STEPS AHEAD: We encourage bold and creative thinking to make breakthroughs in performance. We focus on anticipating and staying ahead of consumer needs and external trends, shifting from reacting to leading, shaping, and disrupting the market. 

• DELIVER WITH EXCELLENCE: The emphasis is on delivering everything with excellence and pace, taking personal ownership, and holding each other accountable. We aim to shift from pride in thinking to pride in execution, developing breakthrough solutions and ensuring the best outcomes. 

Our commitment to Equality, Diversity & Inclusion 

• Unilever embraces diversity and encourages applicants from all walks of life! This means giving full and fair consideration to all applicants and continuing development of all employees regardless of age, disability, gender reassignment, race, religion or belief, sex, sexual orientation, marriage and civil partnership, and pregnancy and maternity.