On-site
Part Time
Job Summary
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 Evaluatio
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.
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