Overall experience – 12+ Yrs
JD-
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.
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.
Key Skillset:
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:
- RAG (Retrieval-Augmented Generation) architectures
- Vector databases (semantic search, embeddings indexing)
- 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:
- Knowledge graphs
- Metadata-driven architectures
- 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 (Google Cloud Platform).
- Familiarity with MLOps practices, including:
- Model versioning
- Deployment pipelines
- 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).
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.
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