At a glanceSummarised by Seekless from the posting.
Must have12
Strong Python programming experience
Hands-on experience with LangGraph
Strong knowledge of Retrieval-Augmented Generation (RAG)
Experience with Prompt Engineering and prompt optimization
Experience developing applications using Generative AI technologies
Knowledge of Elastic Knowledge / Elasticsearch for enterprise search and knowledge retrieval
Experience with LangChain ecosystem
Experience integrating OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or similar LLMs
Knowledge of vector databases such as Pinecone, Chroma, FAISS, Weaviate, or Milvus
Experience building AI agents and multi-step workflows
Strong understanding of REST APIs and microservices
Familiarity with Git, Docker, and CI/CD pipelines
Skills
Python
LangGraph
LangChain
Elasticsearch
Elastic Knowledge
Generative AI
Prompt Engineering
RAG
OpenAI
Azure OpenAI
Anthropic Claude
Gemini
Pinecone
Chroma
FAISS
Weaviate
Milvus
REST APIs
microservices
Git
Docker
CI/CD
About the role
We are looking for a highly skilled AI Engineer with strong expertise in Generative AI, Prompt Engineering, Retrieval-Augmented Generation (RAG), LangGraph, Elastic Knowledge, and Python. The ideal candidate should have experience designing, developing, and deploying enterprise-grade AI applications powered by Large Language Models (LLMs) and modern AI frameworks.
The candidate should be capable of building intelligent AI agents, orchestrating complex workflows using LangGraph, integrating enterprise knowledge bases, and developing scalable GenAI solutions for business use cases.
Key Responsibilities
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Design and develop enterprise Generative AI applications using Python.
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Build and optimize Retrieval-Augmented Generation (RAG) pipelines for knowledge retrieval.
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Develop AI workflows and multi-agent systems using LangGraph.
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Create effective prompts and prompt templates for various LLM use cases.
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Integrate enterprise knowledge repositories using Elastic Knowledge or Elasticsearch-based knowledge systems.
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Develop intelligent AI assistants, chatbots, and agentic AI solutions.
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Connect LLMs with enterprise APIs, databases, and document repositories.
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Optimize retrieval quality, context management, and response accuracy.
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Implement vector search and semantic search capabilities.
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Evaluate, fine-tune, and monitor LLM performance.
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Collaborate with business stakeholders to understand AI use cases and translate them into technical solutions.
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Follow AI governance, security, and responsible AI best practices.
Required Skills
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Strong Python programming experience.
•
Hands-on experience with LangGraph.
•
Strong knowledge of Retrieval-Augmented Generation (RAG).
•
Experience with Prompt Engineering and prompt optimization.
•
Experience developing applications using Generative AI technologies.
•
Knowledge of Elastic Knowledge / Elasticsearch for enterprise search and knowledge retrieval.
•
Experience with LangChain ecosystem.
•
Experience integrating OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or similar LLMs.
•
Knowledge of vector databases such as Pinecone, Chroma, FAISS, Weaviate, or Milvus.
•
Experience building AI agents and multi-step workflows.
•
Strong understanding of REST APIs and microservices.
•
Familiarity with Git, Docker, and CI/CD pipelines.