AI R&D Leadership for Production Systems
Amitai Assayag
AI R&D Leadership for Production Systems
Summary
Led AI R&D for customer-facing cybersecurity and compliance products, turning research ideas into usable production-oriented AI systems.
The work covered AI assistants, LLM/NLP workflows, retrieval systems, anomaly detection, applied machine learning, architecture design, prototyping, implementation, deployment, and customer-facing delivery.
Context
The company needed practical AI capabilities that could move beyond research experiments and become usable product features.
The work required a combination of technical research, software engineering, product judgment, and deployment awareness. The systems had to support real business workflows, not only demonstrate model performance in isolation.
My Role
I led AI R&D as a hands-on technical lead.
Responsibilities included:
Identifying useful AI opportunities for product workflows
Evaluating models, methods, and open-source tools
Designing system architectures
Building prototypes and production-oriented pipelines
Leading a small AI team
Supporting customer-facing AI capabilities
Moving ideas from feasibility research to deployed product use
Technical Scope
Key areas included:
AI assistant systems
LLM and NLP workflows
Retrieval-augmented generation
Vector databases
Anomaly detection
Applied machine learning
Customer-facing AI product features
Research implementation
Model evaluation
Production-oriented ML pipelines
Tools & Technologies
Python
LangChain
LLaMA
TensorFlow
PyTorch
Vector databases
AWS
NLP tooling
Applied ML pipelines
Approach
The work followed a research-to-product workflow:
Opportunity identification
Evaluated where AI could create useful product value in cybersecurity and compliance workflows.
Technical exploration
Tested relevant models, open-source tools, NLP methods, LLM workflows, and applied ML approaches.
Prototype development
Built working prototypes to validate feasibility, user value, and integration complexity.
Architecture design
Designed production-oriented systems that could connect AI components to real product workflows.
Implementation and deployment
Converted successful prototypes into customer-facing capabilities.
Iteration and evaluation
Improved system behavior through model evaluation, engineering refinement, and practical product feedback.
Selected Workstreams
AI Assistant Systems
Built AI assistant capabilities before and after the major LLM adoption wave, combining NLP, retrieval, orchestration, and LLM methods into practical product workflows.
Anomaly Detection
Designed and implemented AI-based anomaly detection systems for cybersecurity and compliance use cases.
LLM and Retrieval Workflows
Developed customer-facing AI capabilities using LLMs, retrieval logic, vector databases, orchestration methods, and production-oriented Python systems.
Applied AI Productization
Evaluated state-of-the-art models and research methods, then adapted them into usable product capabilities rather than isolated demos.
Impact
This work helped move AI capabilities from research exploration into real customer-facing product systems.
It demonstrated the ability to:
Lead AI R&D under practical product constraints
Translate ambiguous AI opportunities into working systems
Combine LLMs, NLP, retrieval, anomaly detection, and applied ML
Build production-oriented AI workflows
Support customer-facing AI delivery
Operate across research, architecture, engineering, and product implementation
Why This Matters
Many AI projects fail between prototype and production.
This project shows the ability to bridge that gap: evaluating what is technically possible, identifying what is useful, building the system, and moving it toward real product use.
Confidentiality Note
Specific product details, customer information, security-sensitive implementation details, and proprietary architecture are omitted.
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Posted May 17, 2026
Led AI R&D for customer-facing systems across LLM assistants, anomaly detection, retrieval workflows, and production-oriented ML pipelines.
