Sr AI Engineer

Job summary

Lead and contribute to the development of AI products, pilots, and enterprise solutions, while designing scalable Generative AI architectures and cloud-native solutions on AWS. Optimize production AI products and drive experimentation with emerging AI methods, frameworks, APIs, and tools.

Job details

• AI Product Delivery: Lead and actively contribute to the development of AI products, pilots, and enterprise solutions with a focus on writing clean, maintainable code. • Architecture & Design: Design and architect scalable Generative AI solutions, including LLM pipelines, Agentic workflows, Model Context Protocol (MCP), Graph/RAG architectures, and complex prompt-based applications. • Cloud Infrastructure: Implement highly scalable, cloud-native solutions leveraging modern AWS services. • Optimization & Innovation: Optimize the performance of production AI products. Drive continuous learning and experimentation with cutting-edge Gen AI methods, frameworks, APIs, and emerging toolchains. • 10 + years of progressive engineering experience. • 1–2 years of direct experience leading emerging technology or enterprise AI initiatives. • Strong familiarity with the full Software Development Life Cycle (SDLC) best practices and DevSecOps protocols. • Generative AI & LLMs: Core proficiency with commercial and open-source models (GPT, Claude, Gemini, LLaMA), advanced prompt engineering, and LLM fine-tuning techniques. • AI Frameworks & Architectures: Deep hands-on experience with LangChain, LlamaIndex, Amazon Bedrock, Agentic AI, MCP, and Graph/RAG architectures.

What you’ll do

Lead and contribute to the development of AI products, pilots, and enterprise solutions, while designing scalable Generative AI architectures and cloud-native solutions on AWS. Optimize production AI products and drive experimentation with emerging AI methods, frameworks, APIs, and tools.

Requirements

Requires 10 or more years of progressive engineering experience and 1–2 years of direct experience leading emerging technology or enterprise AI initiatives. Candidates should have strong SDLC and DevSecOps knowledge, proficiency with commercial and open-source LLMs and advanced prompt engineering, and hands-on experience with LangChain, LlamaIndex, Amazon Bedrock, Agentic AI, MCP, and Graph/RAG architectures.

Listed skills

• Amazon Web Services · Preferred
• prompt engineering · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Generative AI
• Large Language Models
• Prompt Engineering
• LLM Fine-Tuning
• LangChain
• LlamaIndex
• Amazon Bedrock
• Agentic AI
• Model Context Protocol
• Graph/RAG Architectures
• AWS
• Cloud-Native Solutions
• Software Development Life Cycle
• DevSecOps
• AI Product Development
• Scalable System Architecture

Job areas

• Technology
• Software
• Engineering
• Data & Analytics

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