Homeoffice Technology Architect at None

Job Summary

Gen AI Agentic AI Project Management

Python

Designing scalable GenAI systems (e.g. RAG pipelines multi-agent systems).

Choosing between hosted APIs vs open-source models.

Architecting hybrid systems (LLMs + traditional software).

2. Model Evaluation & Selection

Benchmarking models (e.g. GPT-4 Claude Mistral LLaMA).

Responsibilities

Strategic & Leadership-Level GenAI Skills

1. AI Solution Architecture

Designing scalable GenAI systems (e.g. RAG pipelines multi-agent systems).

Choosing between hosted APIs vs open-source models.

Architecting hybrid systems (LLMs + traditional software).

2. Model Evaluation & Selection

Benchmarking models (e.g. GPT-4 Claude Mistral LLaMA).

Using tools like LM Evaluation Harness OpenLLM Leaderboard etc.

3. Enterprise-Grade RAG Systems

Designing Retrieval-Augmented Generation pipelines.

Using vector databases (Pinecone Weaviate Qdrant) with LangChain or LlamaIndex.

Optimizing chunking embedding strategies and retrieval quality.

4. Security Privacy & Governance

Implementing data privacy access control and audit logging.

Aligning with frameworks like NIST AI RMF EU AI Act or ISO/IEC 42001.

5. Cost Optimization & Monitoring

Estimating and managing GenAI inference costs.

Using observability tools (e.g. Arize WhyLabs PromptLayer).

Token usage tracking and prompt optimization.

Advanced Technical Skills

6. Model Fine-Tuning & Distillation

Fine-tuning open-source models using PEFT LoRA QLoRA.

Knowledge distillation for smaller faster models.

Using tools like Hugging Face Axolotl or DeepSpeed.

7. Multi-Agent Systems

Designing agent workflows (e.g. AutoGen CrewAI LangGraph).

Task decomposition memory and tool orchestration.

8. Toolformer & Function Calling

Integrating LLMs with external tools APIs and databases.

Designing tool-use schemas and managing tool routing.

Team & Product Leadership

9. GenAI Product Thinking

Identifying use cases with high ROI.

Balancing feasibility desirability and viability.

Leading GenAI PoCs and MVPs.

10. Mentoring & Upskilling Teams

Training developers on prompt engineering LangChain etc.

Establishing GenAI best practices and code reviews.

Leading internal hackathons or innovation sprints.