What are the responsibilities and job description for the Gen AI Architect position at THOUGHT BYTE?
Role: Gen AI Architect
Location:- Columbus, OH; Cleveland, OH; Kansas City, KS; or Albany, NY. (Hybrid)
10 years experience
JD-
Job Description
Role Summary
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.
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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).