Senior AI Architect (Pre-Sales)

Job Title: Senior AI Architect (Pre-Sales)
W2 role

Experience: 12 15 years

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, Google Cloud Platform). 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.
  • Google Cloud Platform: 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 Google Cloud Platform 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.