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.