PocketPills - AI Engineer - Agentic AI & LLM Applications
PocketPills - AI Engineer - Agentic AI & LLM Applications
PocketPills- Posted an hour ago
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Job Description
About The Role
We're seeking an experienced AI Engineer to design and deploy production-grade LLM applications and AI agents. You'll transform complex AI solutions from prototype through production, building scalable agentic workflows and intelligent systems that drive real-world impact.
Key Responsibilities
(ref:hirist.tech)
We're seeking an experienced AI Engineer to design and deploy production-grade LLM applications and AI agents. You'll transform complex AI solutions from prototype through production, building scalable agentic workflows and intelligent systems that drive real-world impact.
Key Responsibilities
- Design and implement intelligent agents and workflows using frameworks like LangChain, LangGraph, or similar technologies.
- Engineer sophisticated multi-step workflows incorporating tool calling, structured outputs, state management, memory systems, and dynamic loops.
- Develop robust RAG systems including embeddings, vector search, retrieval optimisation, and intelligent reranking.
- Build comprehensive evaluation frameworks to assess accuracy, reliability, latency, and cost efficiency.
- Establish production-ready observability, guardrails, logging, and error handling mechanisms.
- Own the end-to-end journey of AI solutions from initial prototype to full-scale production deployment.
- Python proficiency is essential.
- Strong software engineering fundamentals: OOP principles, asynchronous programming, REST APIs, and robust error handling.
- Proven hands-on experience with LLM and AI agent development.
- Expertise in prompt engineering, loop design, and agentic graph architecture.
- Advanced knowledge of tool calling, function calling, structured outputs, and context/memory management.
- Deep understanding of RAG systems, embeddings, vector search, and retrieval techniques.
- Production experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks.
- Proficiency with REST APIs and backend service architecture.
- Experience with vector databases (pgvector, Pinecone, Weaviate, Qdrant, or similar).
- Hands-on Docker and cloud infrastructure expertise.
- Experience with LangSmith or equivalent observability platforms.
- Knowledge of Model Context Protocol (MCP).
- Expertise in agent evaluation, benchmarking, and automated testing.
- Familiarity with event-driven and asynchronous architectural patterns.
(ref:hirist.tech)
More Info
Key Skills
LangChain
backend service architecture
vector search
embeddings
pgvector
RAG systems
Qdrant
vector databases
Pinecone
LangGraph
retrieval techniques
Weaviate
LlamaIndex
