Generative AI Architect

Generative AI Architect

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• Strategic Leadership & Architecture:
  • Define GenAI strategy and roadmap:
    Align GenAI initiatives with business priorities, identifying opportunities for high-value automation, augmentation, and transformation.
  • Solution architecture ownership:
    Design secure, scalable, and cost-effective GenAI architectures using AWS Bedrock for model orchestration and integration.
  • Tool & model evaluation:
    Select appropriate foundation models (e.g., Anthropic Claude, Amazon Titan, Meta Llama, Stability AI) based on business use cases, data privacy, and latency needs.
  • AI governance and compliance:
    Establish guardrails for responsible AI, data governance, and model lifecycle management in AWS environments.
  • Architecture patterns:
    Define and enforce reusable architecture blueprints (e.g., RAG, agentic workflows, chat-with-enterprise-data patterns).
• Hands‑On Technical Expertise (AWS & GenAI Stack):
  • AWS Bedrock expertise:
    Deploy and integrate foundation models using the Bedrock API. Configure model parameters, embeddings, and inference pipelines. Manage cross‑model orchestration and multi‑agent workflows.
  • RAG implementation:
    Design retrieval‑augmented generation pipelines using Amazon Kendra, Open Search, or vector databases (e.g., Pinecone, Weaviate, FAISS). Build context‑aware conversational systems integrating enterprise data.
  • Agent development:
    Create custom AI agents and copilots using AWS Bedrock Agents or Lang Chain with Bedrock integration. Leverage AWS Lambda, Step Functions, or Sage Maker for agent orchestration and automation.
  • Integration and APIs:
    Build APIs and applications connecting Bedrock with AWS services like S3, Dynamo
    
    DB, Redshift, Aurora, and Event Bridge. Integrate GenAI features into enterprise tools (e.g., Service Now, Jira, Salesforce).
  • Prompt engineering & tuning:
    Optimize prompt templates and chain‑of‑thought frameworks for model accuracy and reliability.
  • Model customization:
    Fine‑tune or adapt foundation models with domain‑specific data via Bedrock or Sage Maker.
• Leadership, Collaboration & Delivery:
  • Lead cross‑functional AI delivery teams (data engineers, ML engineers, cloud architects, developers).
  • Translate business needs into AI solutions with clear ROI, feasibility, and delivery timelines.
  • Mentor and enable engineers on AWS Bedrock and GenAI frameworks (Lang Chain, Semantic Kernel, Llama Index).
  • Communicate technical strategy to senior leadership, articulating value and innovation.
  • Drive PoCs and pilots to production‑grade AI systems across use cases like knowledge assistants, automation bots, or predictive analytics.
• Technical
  
  Skills:
  • Proficiency in programming languages such as Python and Node.js.
  • Expertise in AI and ML frameworks:
    Tensor Flow, PyTorch, scikit‑learn.
  • Experience with RPA tools:
    UiPath, Automation Anywhere, Blue Prism.
  • Knowledge of Dev Ops tools:
    Jenkins, Docker, Kubernetes, Terraform, Ansible.
  • Familiarity with cloud platforms: AWS, Azure, Google Cloud.
  • Experience in using Gen AI APIs and libraries.
  • Minimum 8 years of total experience.
  • Minimum 4 years of programming experience.
  • Proven track record of successfully delivering complex AI (Gen AI) and automation projects.
  • Strong understanding of Dev Ops practices and tools.
• Roles & Responsibilities:
  • AI and ML Development:
    Design, develop, and implement advanced AI and ML models using frameworks such as Tensor Flow, PyTorch, and scikit‑learn.
  • Research:
    Conduct research and stay updated with the latest advancements in AI and ML technologies.
  • Collaboration:
    
    Build and deploy scalable AI solutions with data scientists and engineers.
  • Automation:
    Develop and maintain automation scripts and tools to streamline workflows and improve efficiency. Implement RPA using tools like UiPath, Automation Anywhere, and Blue Prism. Integrate AI‑driven automation solutions to enhance business processes.
  • Dev Ops:
    Manage CI/CD pipelines for AI/ML models. Implement IaC using Terraform and Ansible. Monitor and optimize system performance for high availability and reliability.
  • Leadership:
    Lead cross‑functional teams to deliver AI and automation projects on time and…