GenAI Architect

3 days ago

Dallas, Texas, United States Info Way Solutions Full time $180,000 - $300,000 per year

GenAI Architect – Job Description

Job Title:
Generative AI (GenAI) Architect

Experience:
10+ years in software/AI engineering, with 3–5 years in AI/ML and hands-on experience in LLM/GenAI solutions

Location:
Open

Type:
Full-time

Role Overview

We are seeking a highly skilled
GenAI Architect
to lead the design, development, and implementation of enterprise-grade Generative AI solutions. The ideal candidate will drive AI strategy, architect LLM-based applications, evaluate model options (open-source & commercial), ensure scalability, and guide teams in building secure, production-level AI systems.

Key Responsibilities

1. GenAI Solution Architecture

  • Design end-to-end GenAI system architecture including model selection, orchestration layers, embeddings, vector databases, and RAG pipelines.
  • Architect LLM-powered applications such as chatbots, copilots, automation agents, and knowledge assistants.
  • Define architectural blueprints, best practices, and reusable components for scalable GenAI systems.

2. Model Strategy & Development

  • Evaluate and fine-tune LLMs (OpenAI, Anthropic, Llama, Mistral, Phi, etc.).
  • Build retrieval-augmented generation (RAG) pipelines, prompt engineering frameworks, and guardrail systems.
  • Lead POCs to validate GenAI feasibility for business use cases.

3. Data Engineering & Vector Store Design

  • Architect embeddings, chunking strategies, indexing, and retrieval workflows.
  • Work with vector DBs like
    Pinecone, Weaviate, Milvus, Qdrant, Elasticsearch
    , and cloud-native vector stores.
  • Manage data ingestion pipelines, ETL/ELT workflows, and data governance.

4. Application Integration

  • Integrate GenAI solutions with enterprise apps (Salesforce, ServiceNow, SAP, Workday, custom APIs).
  • Build Python-based microservices, event-driven systems, and inference endpoints.
  • Work with MLOps, DevOps, and cloud teams to deploy scalable models.

5. Security, Governance & Compliance

  • Implement safety filters, ethical AI frameworks, dataset governance, and monitoring.
  • Ensure compliance with enterprise security, PII handling, and risk assessment standards.
  • Apply prompt moderation, red-teaming strategies, and evaluation frameworks.

6. Collaboration & Leadership

  • Work closely with business stakeholders to translate use cases into technical designs.
  • Provide technical leadership to ML engineers, data engineers, and application developers.
  • Create documentation, run workshops, and guide teams to adopt GenAI practices.

Required Skills & Experience

Technical Skills

  • Strong hands-on experience with
    LLMs
    ,
    RAG
    ,
    transformers
    ,
    vector databases
    , embeddings, and tokenization.
  • Expertise in
    Python
    ,
    FastAPI
    ,
    LangChain
    ,
    LlamaIndex
    , or similar GenAI frameworks.
  • Deep understanding of cloud platforms:
    AWS Bedrock, Azure OpenAI, GCP Vertex AI
    .
  • Strong knowledge of
    ML Ops
    , CI/CD, model evaluation, monitoring, and observability.
  • Familiarity with container orchestration:
    Docker, Kubernetes, KubeFlow, SageMaker
    .

Architecture Skills

  • Experience creating
    high-level & low-level architecture diagrams
    , reference architectures, and integration patterns.
  • Ability to evaluate trade-offs between open-source vs. proprietary LLMs.
  • Experience in microservices, event-driven architecture, API gateways, and security protocols (OAuth2, JWT).

Soft Skills

  • Strong analytical and problem-solving skills.
  • Ability to communicate AI concepts to both technical and non-technical stakeholders.
  • Leadership and mentoring experience.

Preferred Qualifications

  • Master's degree in Computer Science, Data Science, AI/ML, or related field.
  • Certifications in cloud or AI (AWS, Azure, GCP, DataBricks).
  • Experience with
    multi-agent AI systems
    ,
    auto-evaluators
    , or
    enterprise AI copilots
    .
  • Experience designing cost-efficient AI architectures at scale.

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