AI Software Architect / Senior AI Engineer (LLM & Agentic Systems)

Position Overview

The ideal candidate combines strong software engineering fundamentals with demonstrated experience delivering real-world AI products. Beyond experimentation, this individual must be capable of designing scalable, reliable, and cost-effective LLM-powered solutions that operate successfully in production environments. They should possess a strong understanding of modern AI architecture patterns, prompt engineering, retrieval systems, agent orchestration, and AI observability.

Key Responsibilities

• Own the architecture of AI-powered services and their integration with backend, mobile, and web applications.
  
• Design, build, and maintain production-grade LLM applications using modern AI frameworks and orchestration platforms.
  
• Lead technical design reviews and drive architectural decisions across AI services, backend systems, data pipelines, and cloud infrastructure.
  
• Architect agentic workflows involving tool use, function calling, multi-agent systems, planning, memory management, and reasoning chains.
  
• Establish evaluation frameworks to measure AI quality, reliability, latency, cost, and business impact.
  
• Implement AI observability and monitoring using platforms such as Langfuse, LangSmith, OpenTelemetry, and related tooling.
  
• Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases, embeddings, document pipelines, and retrieval optimization techniques.
  
• Collaborate with software engineers, data scientists, product managers, and domain experts to translate business requirements into AI solutions.
  
• Optimize prompts, retrieval strategies, model selection, and system architecture for accuracy, reliability, performance, and cost efficiency.
  
• Design scalable APIs and services to expose AI capabilities across internal and external applications.
  
• Define AI engineering standards, best practices, and governance processes across the organization.
  
• Provide technical leadership and mentorship to engineers working on AI initiatives.
  
• Leverage cloud platforms such as Google Cloud Platform (GCP) to deploy and scale AI services.
  
• Document AI architectures, workflows, evaluation methodologies, and operational procedures.
  

Qualifications

• Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or related field.
  
• 10+ years of software engineering experience with at least 5 years focused on LLM-based solutions and generative AI systems.
  
• Demonstrated experience designing and deploying complex production-grade AI applications using Large Language Models.
  
• Extensive experience with AI orchestration frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, or equivalent technologies.
  
• Hands-on experience implementing AI observability and evaluation frameworks using Langfuse, LangSmith, or similar platforms.
  
• Proven expertise with Retrieval-Augmented Generation (RAG) architectures and vector database technologies.
  
• Strong understanding of modern LLM architectures, prompting strategies, context management, embeddings, and retrieval techniques.
  
• Strong Python development experience and software engineering fundamentals.
  
• Experience designing scalable APIs and cloud-native architectures.
  
• Ability to evaluate architectural trade-offs involving model performance, latency, reliability, maintainability, and cost.
  
• Experience deploying AI solutions in production environments using Docker, Kubernetes, and cloud platforms.
  
• Strong understanding of structured and unstructured data processing pipelines.
  
• Familiarity with modern database technologies including PostgreSQL, vector databases, and document stores.
  
• Excellent communication, leadership, and mentoring skills and ability to collaborate effectively with cross-functional teams.
  

Preferred Skills

• Experience building agentic systems involving tool use, planning, memory, and multi-agent orchestration and skills.
  
• Experience with model evaluation, benchmarking, AI testing frameworks, and automated quality assessment.
  
• Experience working with multiple commercial and open-source models including OpenAI, Anthropic, Gemini, Llama, and Mistral.
  
• Familiarity with fine-tuning, synthetic data generation, and model optimization techniques.
  
• Familiarity with developing and training machine learning models.
  
• Experience supporting AI products in regulated, privacy-sensitive, or high-availability environments.
  
• Experience integrating AI capabilities into mobile and web applications.
  
• Familiarity with modern software delivery practices including DevOps, CI/CD, and Agile development methodologies.