Technical Architect

Responsibilities

Core AI/ML Fundamentals

Solid understanding of AI/ML concepts including:

Classification, regression, neural networks
OCR and transcription systems
Audio/Video processing and multimodal learning

OCR, Transcription & Audio/Video Intelligence

Implement specialized models for:

High‑accuracy document OCR
Real‑time audio transcription

Architect deep learning pipelines for audio/video analysis and generation.
Integrate multimodal models (e.g., LLaVA, Whisper) into broader GenAI systems.

Generative AI & LLM Expertise

Strong understanding of:

Generative AI techniques
Transformer architectures
RAG (Retrieval-Augmented Generation) pipelines
Modern LLM ecosystems

Hands-on experience with:

LLM parameter handling and model selection
Scaling strategies and performance optimization

Expertise in:

Prompt engineering and instruction tuning
Prompt tuning and optimization for high-quality outputs

Familiarity with evaluation frameworks covering:

Quality, grounding, accuracy, safety
Latency and cost analysis
Governance and compliance requirements

Agentic AI Systems

Experience designing and building agentic AI systems including:

Multi‑agent orchestration
Tool‑use workflows
Autonomous task execution

Design long‑term memory architectures:

Vector-based memory systems
Graph-based memory for complex, persistent context

Model Training, Fine‑Tuning & Optimization

Knowledge of fine‑tuning approaches:

LoRA, QLoRA, supervised fine-tuning

Experience with model compression techniques:

Quantization, distillation

Familiarity with performance-level tooling:

CUDA, Triton, or specialized custom kernels

Design AI systems capable of efficiently handling and routing multiple user requests simultaneously, including:

Scalable request handling
Load‑balanced inference
Multi‑tenant model utilization
Caching and prioritization strategies

Infrastructure, Pipelines & Deployment

Oversee:

Data pipeline integration
Training workflows
ML CI/CD processes

Strong understanding of:

GPU/compute requirements
Cost‑efficient deployment strategies

Experience designing and managing production-grade inference servers using:

vLLM
Text Generation Inference (TGI)
SGLang

Ability to collaborate with engineering teams to integrate LLMs into production systems:

APIs, microservices, cloud architectures

Research, Evaluation & Continuous Innovation

Stay current with advancements in AI, ML, and LLM ecosystems.
Evaluate new tools, frameworks, and platform technologies to continuously enhance system architecture.