AI Prompt Engineering Consultant

AI Prompt Engineering Consultant, Technically Sharp & Systems-Minded

Deesign and optimize prompts, architect LLM-powered systems and deploy scalable GenAI workflows that connect people and intelligent systems in new, high-impact ways.

THE ROLE

Prompting & Reasoning Systems

• Design, test and optimize prompts for leading frontier models (GPT-4/5, Claude 3.x, Gemini 2.x, Mistral Large, LLaMA 3, Cohere Command R+, DeepSeek).
• Apply advanced prompting strategies:
• Chain-of-Thought, ReAct, Tree-of-Thoughts, Graph-of-Thoughts, Program-of-Thoughts, self-reflection loops, debate prompting and multi-agent orchestration (AutoGen/CrewAI).
• Build agentic workflows with tool calling, memory systems, retrieval pipelines and structured reasoning.

GenAI Application Engineering

• Integrate LLMs into applications using LangChain, LlamaIndex, Haystack, AutoGen and OpenAI s Assistant API patterns.
• Build high-performance RAG pipelines using:
• hybrid search, reranking, embedding optimization, chunking strategies and evaluation harnesses.
• Develop APIs, microservices and serverless workflows for scalable deployment.

ML/LLM Engineering

• Work with AI+ML pipelines through Azure ML, AWS SageMaker, Vertex AI, Databricks, or Modal/Fly.io for lightweight LLM deployment.
• Utilize vector databases (Pinecone, Weaviate, Milvus, ChromaDB, pgVector) and embedding stores.
• Use AI-powered dev tools (GitHub Copilot, Cursor, Codeium, Aider, Windsurf) to accelerate iteration.
• Implement LLMOps/PromptOps using:
• Weights & Biases, MLflow, LangSmith, LangFuse, PromptLayer, Humanloop, Helicone, Arize Phoenix
• Benchmark and evaluate LLM systems using Ragas, DeepEval and structured evaluation suites.

Deployment & Infrastructure

• Containerize and deploy workloads with Docker, Kubernetes, KNative and managed inference endpoints.
• Optimize model performance with quantization, distillation, caching, batching and routing strategies.

EXPERIENCE

• Strong Python skills, with experience using Transformers, LangChain, LlamaIndex and the broader GenAI ecosystem and prompt engineering experience.
• Deep understanding of LLM behavior, prompt optimization, embeddings, retrieval and data preparation workflows.
• Experience with vector DBs (FAISS, Pinecone, Milvus, Weaviate, ChromaDB).
• Hands-on knowledge of Linux, Bash/Powershell, containers and cloud environments.
• Strong communication skills, creativity and a systems-thinking mindset.
• Curiosity, adaptability and a drive to stay ahead of rapid advancements in GenAI.

BENEFICIAL

• Experience with PromptOps & LLM Observability tools (PromptLayer, LangFuse, Humanloop, Helicone, LangSmith).
• Understanding of Responsible AI, model safety, bias mitigation, evaluation frameworks and governance.
• Background in Computer Science, AI/ML, Engineering, or related fields.
• Experience deploying or fine-tuning open-source LLMs.

TECH STACK

LLMs: GPT-4/5, Claude 3.x, Gemini 2.x, Mistral Large, LLaMA 3, Cohere Command R+, DeepSeek

Frameworks: LangChain, LlamaIndex, Haystack, AutoGen, CrewAI

Tools: GitHub Copilot, Cursor, LangSmith, LangFuse, Weights & Biases, MLflow, Humanloop

Cloud: Azure ML, AWS SageMaker, Google Vertex AI, Databricks, Modal

Infra: Python, Docker, Kubernetes, SQL/NoSQL, PyTorch, FastAPI, Redis

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