Gen AI Architect

The GenAI Architect is responsible for designing, guiding, and implementing enterprise-grade Generative AI solutions embedded within product platforms. This role bridges AI research, engineering, and product development, ensuring GenAI capabilities are scalable, secure, and aligned with business objectives.

Key Responsibilities

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

• Guide engineering teams on best practices for GenAI development and integration.

• Establish standards and patterns for prompts, agents, and inference layers.

• Ensure security, compliance, and responsible AI principles are built into every solution.

Required Qualifications

• 8+ years of experience in software architecture, ML engineering, or platform engineering.

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

• Strong software engineering background (Python, Java, or similar).

• Prior work with enterprise AI governance or regulated industries.

• Familiarity with open-source AI ecosystems.

• Background in data platforms or analytics engineering.

Core Generative AI & ML Expertise

• Strong understanding of Generative AI models (LLMs, multimodal models, embeddings).

• Hands-on experience with foundation models (e.g., GPT-style, Claude-style, LLaMA-style) and model adaptation techniques.

• Expertise in prompt engineering, prompt orchestration, and agent-based frameworks.

• Solid grounding in machine learning fundamentals, including supervised/unsupervised learning, evaluation metrics, and inference optimization.

AI Architecture & System Design

• Ability to design scalable, modular GenAI architectures for production use.

• Experience with:

o RAG (Retrieval-Augmented Generation) architectures

o Vector databases (semantic search, embeddings indexing)

o Multi-agent systems and workflow orchestration

• Strong understanding of low-latency inference, model routing, and fallback strategies.

• Knowledge of event-driven, microservices, and API-first architectures.

Product Engineering & Integration

• Experience integrating GenAI capabilities into customer-facing and internal products.

• Ability to translate product requirements into AI-driven capabilities and technical designs.

• Familiarity with A/B testing, feature flags, and iterative product releases involving AI.

Data & Knowledge Engineering

• Proficiency in data pipelines, feature engineering, and unstructured data processing.

• Experience with:

o Knowledge graphs

o Metadata-driven architectures

o Document ingestion and chunking strategies

• Strong understanding of data quality, provenance, and governance for AI systems.

Cloud, MLOps & Platform Skills

• Strong experience in cloud-native environments (GCP).

• Familiarity with MLOps practices, including:

o Model versioning

o Deployment pipelines

o Monitoring, logging, and drift detection

• Experience with containerization, Kubernetes, and CI/CD pipelines.

• Knowledge of inference optimization and cost-control strategies.

Security, Privacy & Responsible AI

• Understanding of AI security risks (prompt injection, data leakage, model abuse).

• Experience implementing guardrails, content filters, and policy enforcement.

• Knowledge of responsible AI practices, including explainability, bias mitigation, and compliance.

• Familiarity with data privacy regulations (e.g., GDPR, enterprise governance standards).