Principal Solutions Architect

Principal AI Tech ArchitectsPrincipal AI Tech Architects at BCG Platinion are:Collaborative. They are interdisciplinary team players who seek alignment and establish relationships ranging from cross-functional stakeholder groups to existing teams.Systems thinkers. They are well-versed in designing sophisticated and robust AI solutions to common business problems.Technical experts. They are critical thinkers and have extensive Technology & AI expertise that drives novel solutions.Comfortable with ambiguity. They know the path forward isn't always well-defined. They are comfortable and confident working through the unknown.Change agents. They know how to make change happen across an organization. They can align and onboard teams to implement new processes and toolsets. They embrace complex challenges and guide an organization to optimize technology best practices.Agile advocates. They are well-versed in agile methodologies and use their expertise to strategically tackle difficult challenges and implement change.Innovative. They are creative thinkers who apply their deep technology architecture and AI Platform expertise to envision novel design patterns for the next generation of technology solutions.Knowledgeable. They have a sophisticated understanding of Technology Architecture and AI Platforms, acting as a viable resource on teams as both a hands-on practitioner and subject matter expert.Strategic partners. They work closely with senior client stakeholders, including C-level executives, to build hypothesis-driven solutions at the intersection of technology and business. They bridge the gap between IT and business functions, ensuring technology strategies are aligned with organizational goals to solve critical business challenges.We are seeking Principal AI Tech Architects to design, guide, and evolve the architecture of next-generation intelligent agentic systems. This role bridges high-level system design with hands-on AI engineering - ideal for someone who can envision complex AI-enabled ecosystems while still being comfortable prototyping or coding to validate ideas. You'll work across AI, data, product, and engineering teams to define scalable architectures for AI-driven applications, enabling the seamless integration of large language models (LLMs), data pipelines, and enterprise systems.AI Architecture & Solution DesignDefine system architectures for AI and LLM-based solutions, ensuring scalability, low-latency inference, and modularity.Design Agentic AI solutions to optimize business workflow automation, including the use of memory, agentic reflection, and task planning.Design AI integration patterns, such as RAG, for model orchestration, context management, prompt orchestration, and external system connectivity.Establish architectural blueprints, design principles, and governance frameworks for model versioning, data lineage and responsible AI solutions across the organization.Architect polyglot model runtimes and hybrid pipelines (batch training + streaming inference) with attention to cost, latency, and risk.Prototyping & ValidationDevelop minimal viable prototypes or proof-of-concepts to test model architectures, RAG flows, and agent orchestration.Evaluate emerging AI frameworks and tools (e.g., LangChain, LlamaIndex, LangGraph, Strands, Google ADK, Semantic Kernel) for suitability and integration.Develop solutions using the latest agentic orchestration engines, such as n8n, Retool, OpenAI Agentkit, Microsoft Copilot Studio, and AWS Flows.Implementation management and supportSupport restructuring IT processes and organization to adopt new systems.Oversee system performance, observability, and governance as solutions transition from prototype to production using tools such as LangSmith, Prometheus, Grafana, Evidently, WhyLogs, and OpenTelemetry.Optimize the application development processes - define standards for versioning, testing, and monitoring of models and AI services (LLMOps).Communication and CollaborationCraft compelling narratives for Agentic reimagination of businesses, translating complex ideas for both technical and non-technical audiences.Deliver impactful presentations, leading modules, presenting findings, and building consensus with clients.Build collaboration, mentor teams, and share knowledge to drive collective success - partner with product managers, data scientists, and engineers to translate business goals into robust AI system designs.Facilitate technical and strategic working sessions and workshops with both client and internal teams - provide technical direction and mentorship to engineering teams on architectural patterns, scalability, and best practices.Provide directions on key work items and feedback to other team members.Team ManagementManage junior team members by defining and organizing their "module", helping them structure their work and associated analyses.Mentor and coach junior team members by building mutual development agreements and manufacturing opportunities for them to grow and develop along explicitly defined objectives.Provide quality assurance oversight - Review junior team members' output for overall correctness, level of insight, clarity, etc.Promote an overall positive experience for junior staffServe as a role model by actively demonstrating and living BCG's Culture and Values.Innovation and GrowthStay ahead of the curve on new techniques in distributed AI systems, retrieval-augmented generation (RAG), retrieval/indexing strategies, and agent frameworks.Drive innovation, uncovering novel ideas and transformative solutions.Serve as the subject matter expert on LLM integration, agentic system design, and intelligent automation frameworks.Foster a growth mindset, continuously learning and applying new skills to grow your impact.Support business development through technical proposals with clear AI/ML technology choices and roadmaps.Manage mid-to-senior level client relationships autonomously.Contribute to our thought leadership through written publications and speaking at events and conferences.What You'll BringBachelor's degree in information technology, business administration, computer science, information systems, or system controls & engineering or a related field (Master's degree is a plus).10+ years of technology consulting or software engineering experience with more than 5+ years in architecture discipline.Strong foundation in software architecture and distributed systems design.Proven experience designing or scaling AI/ML or LLM-based applications.Proficiency in one or more programming languages: Python, JavaScript, TensorFlow, Keras, PyTorch, TypeScript, or Node.js.Familiarity with modern AI frameworks (LangChain, LangGraph, Google ADK, Strands, Semantic Kernel, AutoGen, CrewAI, LlamaIndex).Understanding of Agentic AI capabilities and tools such as LangChain agents, AutoGen, multi-agent orchestration, RAG, memory modules, vector DBs, function-calling, observability, safety, deployment strategies and governance.Understanding of cloud architecture, data management, and API-based system integrationCloud platforms: AWS (Bedrock, AgentCore), Microsoft (Azure AI Foundry, Copilot Studio), Google (Vertex AI, ADK, Agentspace).Design and deployment of autonomous and semi-autonomous agents across multi-cloud and hybrid environments.Integration of agent workflows with enterprise APIs, knowledge bases, and vectorized memory stores.Containerization and orchestration: Docker, Kubernetes, Helm, Istio with GPU autoscaling for agentic runtime optimization.Experience with AI coding tools such as Cursor, Replit, Lovable, Claude code, Codex etc.Experience with vector databases, embeddings, context retrieval, and prompt orchestration patterns. Exposure to Pinecone, QDrant, Cosmos DB, Azure Search is a plus.Experience with knowledge graphs such as Neo4j and knowledge representation techniques is a plus.Exposure to Agentic memory engines such as mem0, Redis Memory, Postgres Memory is a plus.Outstanding ability to work creatively, autonomously, analytically, in a fast-paced problem-solving environment with a focus on customer and results.Superior technical knowledge, engineering rigor, and creative problem-solving.Ability to explain complex technology topics to senior executives.Ideally, experience with the below technologies:Multi-agent systems, RAG, or knowledge graph integration.MLOps / LLMOps pipelines, observability, and evaluation frameworks.Frontend