GenAI Architect Enterprise Generative AI



Work Location: Remote

Work Auth: All Work Auth Accepted (No h1) and no Fake Profile

Job Summary

We are looking for an experienced GenAI Architect to design, guide, and implement enterprise-grade Generative AI solutions embedded within modern product platforms.

This role will bridge AI research, software engineering, platform architecture, and product development, ensuring Generative AI capabilities are scalable, secure, production-ready, and aligned with business objectives.

The ideal candidate will have strong hands-on expertise in LLMs, RAG, agentic AI, vector databases, AI architecture, Google Cloud Platform, MLOps, data engineering, AI governance, and responsible AI.

Required Experience

• 8+ years of experience in Software Architecture, ML Engineering, Platform Engineering, or related areas.

• 2+ years of hands-on AI/ML experience, including Generative AI solutions.

• Strong software engineering background using Python, Java, or similar programming languages.

• Experience working with enterprise AI governance, regulated environments, or compliance-driven applications.

• Experience with open-source AI/ML ecosystems.

• Strong background in data platforms, analytics engineering, or data-intensive architectures.

Core Generative AI & ML Skills

• Strong understanding of Generative AI, Large Language Models (LLMs), multimodal models, and embeddings.

• Hands-on experience with foundation models such as:

  • GPT

  • Claude

  • LLaMA

  • Similar enterprise or open-source LLMs

• Experience with model adaptation, prompt engineering, prompt orchestration, and agentic AI frameworks.

• Strong understanding of machine learning concepts including:

  • Supervised and unsupervised learning

  • Model evaluation

  • Inference optimization

  • Performance metrics

AI Architecture & System Design

• Proven ability to design scalable, modular, production-grade GenAI architectures.

• Strong experience with:

  • RAG Retrieval-Augmented Generation

  • Vector databases

  • Semantic search

  • Embedding generation and indexing

  • Multi-agent systems

  • Agent/workflow orchestration

• Experience designing low-latency inference architectures.

• Knowledge of model routing, fallback mechanisms, caching, and inference optimization.

• Strong understanding of:

  • Microservices architecture

  • Event-driven architecture

  • API-first system design

Product Engineering & Integration

• Experience integrating Generative AI capabilities into customer-facing and internal enterprise applications.

• Ability to translate business and product requirements into AI-powered features and technical architectures.

• Experience working closely with Product, Engineering, Data, Security, and Platform teams.

• Familiarity with:

  • A/B testing

  • Feature flags

  • Controlled AI rollouts

  • Iterative product releases

Data & Knowledge Engineering

• Strong experience with data pipelines, feature engineering, and unstructured data processing.

• Experience with:

  • Knowledge graphs

  • Metadata-driven architectures

  • Document ingestion

  • Document parsing and preprocessing

  • Chunking strategies

  • Embedding pipelines

• Strong understanding of data quality, lineage, provenance, governance, and access control for enterprise AI applications.

Cloud, MLOps & Platform Engineering

• Strong experience with Google Cloud Platform (Google Cloud Platform) and cloud-native architectures.

• Knowledge of MLOps practices including:

  • Model versioning

  • Model deployment

  • CI/CD pipelines

  • Monitoring and observability

  • Logging

  • Model drift detection

• Experience with:

  • Docker / Containerization

  • Kubernetes

  • CI/CD

  • Cloud-native deployment patterns

• Experience optimizing LLM inference performance, scalability, latency, and cost.

Security, Privacy & Responsible AI

• Strong understanding of Generative AI security risks including:

  • Prompt injection

  • Data leakage

  • Model abuse

  • Unauthorized data exposure

• Experience implementing:

  • AI guardrails

  • Content filtering

  • Policy enforcement

  • Access controls

• Knowledge of Responsible AI, including:

  • Explainability

  • Bias mitigation

  • AI governance

  • Model risk management

  • Compliance

• Familiarity with GDPR and enterprise data privacy/governance standards.

Key Responsibilities

• Design and own the end-to-end architecture for enterprise GenAI solutions and AI-powered product features.

• Define technical architecture, integration patterns, and platform standards for Generative AI applications.

• Guide engineering teams on GenAI development, architecture, implementation, and production best practices.

• Establish reusable standards and frameworks for:

  • Prompts

  • Agents

  • RAG pipelines

  • Model integrations

  • Inference layers

• Architect scalable RAG, agentic AI, knowledge retrieval, and multi-model solutions.

• Evaluate and select foundation models, vector databases, orchestration frameworks, and AI platform technologies.

• Partner with Product and Engineering teams to translate business requirements into production-ready AI capabilities.

• Ensure GenAI systems meet requirements for scalability, performance, availability, latency, security, and cost efficiency.

• Implement architecture standards for AI governance, observability, monitoring, and model lifecycle management.

• Ensure security, privacy, compliance, and Responsible AI principles are incorporated throughout the AI solution lifecycle.

• Provide architectural guidance and technical leadership across engineering, ML, data, cloud, and product teams.

Key Skills

GenAI Architecture | LLMs | RAG | Agentic AI | Multi-Agent Systems | Prompt Engineering | Prompt Orchestration | GPT | Claude | LLaMA | Vector Databases | Embeddings | Semantic Search | Knowledge Graphs | Python | Java | Google Cloud Platform | MLOps | Kubernetes | Docker | Microservices | API Architecture | CI/CD | AI Governance | Responsible AI | AI Security | Data Engineering