AI Prompt Engineer Technically Sharp & Systems-Minded
Youll design and optimize prompts, architect LLM-powered systems and deploy scalable GenAI workflows that connect people and intelligent systems in new, high-impact ways.
What Youll Do
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).
- Buildagentic workflowswith tool calling, memory systems, retrieval pipelines and structured reasoning.
GenAI Application Engineering
- Integrate LLMs into applications usingLangChain,LlamaIndex,Haystack,AutoGen and OpenAIs 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 throughAzure ML,AWS SageMaker,Vertex AI,Databricks, orModal / Fly.iofor lightweight LLM deployment.
- Utilizevector databases(Pinecone, Weaviate, Milvus, ChromaDB, pgVector) and embedding stores.
- UseAI-powered dev tools(GitHub Copilot, Cursor, Codeium, Aider, Windsurf) to accelerate iteration.
- ImplementLLMOps / PromptOpsusing:
- Weights & Biases,MLflow,LangSmith,LangFuse,PromptLayer,Humanloop,Helicone,Arize Phoenix
- Benchmark and evaluate LLM systems usingRagas,DeepEval and structured evaluation suites.
Deployment & Infrastructure
- Containerize and deploy workloads withDocker, Kubernetes, KNative and managed inference endpoints.
- Optimize model performance with quantization, distillation, caching, batching and routing strategies.
Youll Bring
- Strong Python skills, with experience usingTransformers,LangChain,LlamaIndex and the broader GenAI ecosystem.
- 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.
Nice to Have
- Experience withPromptOps & LLM Observabilitytools (PromptLayer, LangFuse, Humanloop, Helicone, LangSmith).
- Understanding ofResponsible 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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