Company
Technology
SeniorAIEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior AI Engineer. Skills: LLM Engineering, Generative AI, RAG, Backend Development. Architect LLM applications. Design LLM applications”
Industry & Context.
What They're Looking For.
Must Have
10+ years software engineering, Backend development expertise, Advanced Python proficiency, FastAPI, Django experience, LLM engineering expertise, Prompt engineering experience, RAG architectures experience, OpenAI or Amazon Bedrock API experience, Elasticsearch experience, Vector search experience, Hybrid search experience, Semantic retrieval experience, Vector databases experience, Pinecone, Qdrant, or ChromaDB experience, Embedding-based retrieval systems experience, Production-grade generative AI systems experience, Copilot-style or agent-based applications experience, Distributed systems understanding, Microservices understanding, Event-driven architecture understanding, Scalable backend design understanding, Cloud platforms experience, AWS or GCP experience, Docker, Kubernetes experience, CI/CD pipelines experience, LLMOps or MLOps familiarity, Communication skills
Nice to Have
Experience operating AI systems at scale, Enterprise-grade reliability requirements exposure
What You'll Do.
Architect LLM applications
Design LLM applications
Develop production-grade LLM applications
Optimize RAG pipelines
Develop agentic workflows
Design backend services
Maintain backend services
Implement LLMOps practices
Implement GenAIOps practices
Implement evaluation frameworks
Implement monitoring systems
Implement CI/CD pipelines
Implement model management
Implement version management
Apply Responsible AI principles
Apply security-by-design practices
Collaborate with cross-functional teams
Shape AI architecture
Mentor engineering peers
Contribute to best practices
How You'll Work.
Team & Collaboration
Cross-functional teams; Product teams; Engineering teams; Security teams
Communication Scope
Technical stakeholders; Non-technical stakeholders
Full Job Description
## Accountabilities Architect, design, and develop production-grade LLM applications including copilots, chatbots, and autonomous AI agents. Build and optimize Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured enterprise data sources. Develop agentic workflows with tool calling, function execution, and multi-step reasoning capabilities. Design and maintain backend services and APIs supporting AI inference, orchestration, and system integration. Implement LLMOps/GenAIOps practices including evaluation frameworks, monitoring systems, CI/CD pipelines, and model/version management. Optimize AI systems for performance, ensuring the right balance of latency, cost efficiency, scalability, and reliability. Apply Responsible AI principles and security-by-design practices across all AI system implementations. Collaborate with cross-functional teams including product, engineering, and security to shape AI architecture and strategy. Mentor engineering peers and contribute to establishing best practices in generative AI system design. Requirements: 10+ years of professional software engineering experience with strong backend development expertise. Advanced proficiency in Python, with experience in frameworks such as FastAPI, Django, and modern backend systems. Hands-on expertise in LLM engineering, including prompt engineering, RAG architectures, and APIs such as OpenAI or Amazon Bedrock. Strong experience with Elasticsearch, including vector search, hybrid search (BM25 + embeddings), and semantic retrieval techniques. Proficiency with vector databases such as Pinecone, Qdrant, or ChromaDB and embedding-based retrieval systems. Proven experience building production-grade generative AI systems, including Copilot-style or agent-based applications. Strong understanding of distributed systems, microservices, event-driven architecture, and scalable backend design. Experience with cloud platforms (AWS/GCP), containerization (Docker, Kubernetes), and CI/C
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