Senior AI Architect

Job Title: Senior AI Architect (Pre-Sales) Experience: 12–15 years Chicago, IL Position Summary An accomplished Senior AI Architect with 12–15 years of progressive experience in designing, building, and delivering enterprise-scale AI and Generative AI (GenAI) solutions across public cloud ecosystems (Azure, AWS, GCP). The ideal candidate will serve as both a technical strategist and client-facing leader, driving AI architecture design, pre-sales solutioning, client engagement, and innovation leadership across diverse industry verticals. This role blends deep technical expertise with strong business acumen — ideal for someone who can translate cutting-edge AI capabilities into measurable business outcomes. Key Responsibilities 1. Solution Architecture & Design • Architect scalable, secure, and high-performance AI/ML and GenAI platforms leveraging cloud-native services and frameworks. • Design end-to-end AI reference architectures, covering data ingestion, model development, deployment, governance, and LLMOps. • Evaluate and integrate LLMs, vector databases, and RAG pipelines for enterprise use cases (e.g., chatbots, copilots, document analyzers, and cognitive automation). 1. Pre-Sales & Business Development • Partner with sales, account, and delivery teams to lead AI opportunity qualification, scoping, and solutioning. • Conduct customer discovery workshops, PoC planning, and technical presentations to articulate AI value propositions. • Prepare solution proposals, cost estimates, effort models, and architecture diagrams tailored to client requirements. • Contribute to RFP/RFI responses, bid defense, and client demonstrations showcasing AI capabilities and differentiators. 1. Client Engagement & Relationship Management • Act as a trusted AI advisor to client executives, helping shape their AI strategy and roadmap. • Manage CXO-level discussions, handle technical objections, and translate complex AI concepts into business-friendly narratives. • Foster long-term relationships with customers to identify expansion opportunities and drive AI adoption maturity. 1. Delivery Governance & Leadership • Oversee architecture reviews, design validation, and technical quality assurance across AI projects. • Collaborate with delivery teams to ensure architecture alignment, scalability, and operational efficiency. • Mentor and guide AI engineers, data scientists, and solution architects, fostering innovation and continuous learning. 1. Innovation & Thought Leadership • Evaluate new AI/GenAI technologies, frameworks, and models (e.g., GPT-4/4o, Claude, Gemini, LLaMA, Mistral). • Develop accelerators, reusable assets, and reference implementations to enhance pre-sales effectiveness. • Represent the organization at industry forums, webinars, and client advisory boards as an AI thought leader. Technical Skills & Competencies • AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain, LlamaIndex, OpenAI API, etc. • GenAI/LLM Expertise: Prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, embeddings, and vector DBs (Pinecone, Weaviate, FAISS, Cosmos DB, Milvus). • Cloud Platforms: • Azure: OpenAI, AI Search, Cognitive Services, Synapse, Databricks, Azure ML. • AWS: Bedrock, SageMaker, Comprehend, Lex, Kendra. • GCP: Vertex AI, BigQuery ML. • MLOps/LLMOps: Azure ML Pipelines, MLflow, Kubeflow, SageMaker Pipelines, CI/CD (GitHub Actions, Jenkins, etc.). • Integration & APIs: REST/GraphQL, microservices, Docker, Kubernetes, Terraform, IaC principles. • Data Engineering: Knowledge of data lakes, feature stores, and streaming systems (Kafka, EventHub). • Business & Pre-Sales Tools: MS Visio, PowerPoint, Excel-based ROI models, cost estimators, proposal templates. Qualifications • Bachelor’s or Master’s degree in Computer Science, Data Science, or Artificial Intelligence. • 12–15 years of total experience, with at least 6+ years in AI architecture & solution design and 3+ years in pre-sales or client-facing roles. • Proven track record in conceptualizing and delivering AI-driven business solutions at enterprise scale. • Certifications in Azure AI Engineer, AWS ML Specialty, or GCP ML Engineer preferred. • Strong presentation, storytelling, and negotiation skills. Preferred Attributes • Experience in building and leading AI CoEs or innovation teams. • Deep understanding of Responsible AI, governance, and model risk frameworks. • Ability to balance technical depth with executive-level communication. • Demonstrated success in winning deals or expanding AI engagements through consultative selling.