Posted:23 hours ago|
Platform:
On-site
Full Time
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). Understanding trade-offs: latency cost accuracy context length. 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. Understanding risks: prompt injection data leakage model misuse. 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.
Cognizant
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