Senior AI Architect

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). 2. 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. 3. 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. 4. 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. 5. 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.AP 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.