AI Architect

Role Title

AI Architect

Experience Level

Twelve Plus Years Overall (Four Plus Years in AI/ML Architecture)

Core Focus

GenAI Architecture & Strategy, Engineering Architecture, MLOps/LLMOps, Customer Engagement, Governance

Role Overview

We are looking for an experienced AI Architect to lead the design and implementation of enterprise-scale AI/GenAI solutions. This role bridges the gap between cutting-edge generative AI capabilities and robust engineering architectures, ensuring our AI systems are scalable, secure, and production-ready.

Key Responsibilities

GenAI Architecture & Strategy

- Define and own the end-to-end architecture for Generative AI solutions including LLM-powered applications, RAG pipelines, agentic workflows, and multi-model orchestration.
- Evaluate, select, and integrate foundation models (commercial and open-source) based on cost, latency, accuracy, and compliance requirements.
- Design prompt engineering frameworks, guardrails, and evaluation strategies for GenAI systems.
- Architect fine-tuning, distillation, and model customization pipelines where needed.
- Stay current with the rapidly evolving GenAI landscape and translate research advancements into practical enterprise solutions.

Engineering Architecture

- Design scalable, resilient, and cost-efficient cloud-native architectures for AI/ML workloads.
- Define reference architectures, design patterns, and technical standards for AI system development across the organization.
- Architect data pipelines, feature stores, vector databases, and model serving infrastructure.
- Ensure non-functional requirements performance, security, observability, disaster recovery — are built into every solution.
- Drive API design, microservices decomposition, and integration patterns for AI-enabled products.

Customer Engagement & Consulting

- Serve as the primary technical advisor for enterprise clients, leading discovery workshops, solution briefings,



and architecture deep-dives.
- Translate client business challenges into well-scoped AI/GenAI solution architectures, producing proposals, statements of work, and technical roadmaps.
- Drive pre-sales and proof-of-concept engagements, demonstrating solution value and accelerating client decision-making.
- Gather and synthesize customer feedback to influence product direction and shape reusable consulting accelerators.
- Represent the organization at client executive briefings, industry conferences, and external AI forums.

Leadership & Governance

- Collaborate with product, engineering, data science, and platform teams to align AI architecture with business goals.
- Establish AI governance frameworks covering model lifecycle management, bias/fairness monitoring, and responsible AI practices.
- Conduct architecture reviews, provide technical mentorship, and build internal capability in AI/GenAI.
- Create and maintain architecture decision records (ADRs), technical documentation, and roadmaps.
- Represent the organization in vendor evaluations, technology partnerships, and industry forums.

Required Qualifications & Skills

- Experience: Twelve plus years in software/data engineering with at least 4+ years in AI/ML architecture roles.
- GenAI Expertise: Hands-on experience building production systems with LLMs (GPT, Claude, Gemini, Llama, Mistral, etc.), RAG architectures, vector stores (Pinecone, Weaviate, pg vector), and agentic frameworks (LangChain, LangGraph, CrewAI, or similar).
- Engineering Architecture: Deep knowledge of distributed systems,



cloud platforms (AWS/Azure/GCP), containerization (Kubernetes, Docker), and CI/CD for ML (MLOps/LLMOps).
- Data & ML Platforms: Experience with ML platforms (SageMaker, Vertex AI, Databricks), data orchestration (Airflow, Dagster), and streaming systems (Kafka, Flink).
- Programming: Strong proficiency in Python; working knowledge of Java, Go, or TypeScript is a plus.
- System Design: Proven ability to design systems that handle scale, fault tolerance, and low-latency inference.
- Communication: Ability to articulate complex technical concepts to both engineering teams and executive stakeholders.

Preferred Qualifications

- Experience with multi-modal AI systems (text, image, audio, video).
- Familiarity with AI safety, alignment, and responsible AI frameworks.
- Exposure to edge/on-device AI deployment.
- Contributions to open-source AI projects or published research.
- Experience in regulated industries (finance, healthcare, government) with compliance-aware AI deployments.

Indicative Tech Stack

Area

Technologies

Foundation Models

OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral

GenAI Tooling

LangChain, LangGraph, LlamaIndex, Semantic Kernel, Haystack

Vector Databases

Pinecone, Weaviate, Qdrant, pgvector, ChromaDB

ML/MLOps

SageMaker, Vertex AI, MLflow, Kubeflow, Weights & Biases

Cloud & Infra

AWS / Azure / GCP, Kubernetes, Terraform, Docker

Data

Spark, Kafka, Airflow, Snowflake, Delta Lake

Observability

Datadog, Grafana, LangSmith, Arize AI

What You'll Influence

- The GenAI strategy and technical direction of the organization.
- Build vs. buy decisions for AI capabilities.
- Engineering culture around AI-first product development.
- Talent development and hiring standards for AI engineering teams.

We are an equal prospect employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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