Solution Architect – Python with GenAI

Our team is expanding, and we're bringing on a Solution Architect with deep specialization in Python and Generative AI to build and deliver enterprise-grade GenAI solutions ready for real-world production use. You'll be an integral part of our Solution Architecture group, spearheading technical exploration efforts, crafting reusable frameworks, and shaping standards for LLM-based applications. Submit your application to take part in forward-thinking GenAI initiatives and empower clients to achieve breakthrough outcomes. Responsibilities Architect complete GenAI systems, including RAG frameworks, Agents, and Multi-Agent configurations, tailored for enterprise-level clients Develop reusable accelerators and reference builds to bring consistency to delivery approaches Facilitate technical discovery workshops while offering direct, practical support to engineering teams Establish evaluation methodologies and set standards for building large language model (LLM) applications Work alongside diverse teams to guarantee system designs that scale effectively and remain easy to maintain Build out microservices-based architectures leveraging established design patterns and contemporary frameworks Manage the incorporation of cloud infrastructure elements across platforms like AWS, Azure, or GCP Handle containerization and orchestration processes utilizing Docker and Kubernetes Assess and choose suitable vector database solutions, such as Pinecone, Weaviate, or Chroma, for GenAI use cases Deploy LLMOps methodologies and monitoring solutions to fine-tune production-level deployments Assist with prompt management and RAG evaluation processes to boost application precision Take part in mentoring efforts and knowledge exchange across the broader architecture team Maintain high standards of production-ready code quality within Python-based applications Push forward ongoing enhancements in system architecture and solution scalability Requirements Between 9 and 14 years of experience in software development, with a strong foundation in solution architecture and system design Advanced Python development skills with a focus on writing production-ready code Skilled in microservices design, architectural patterns, FastAPI, Redis, Elasticsearch, and Kafka Substantial background building GenAI applications, covering Agents, MCP, RAG, Agentic RAG, and GraphRAG Strong familiarity with LangGraph, LangChain, and related orchestration tools Hands-on background in evaluating LLMs and RAG systems, along with managing prompts Demonstrated success in delivering scalable GenAI applications ready for production environments Working experience with cloud ecosystems such as AWS, Azure, or GCP Comfortable with containerization tools, namely Docker and Kubernetes Familiarity with vector database technologies, including Pinecone, Weaviate, or Chroma Solid grasp of LLMOps principles and associated monitoring solutions Strong leadership capabilities suited to directing engineering teams Capacity to thrive in client-facing settings while working collaboratively Solid English communication skills, both written and spoken, at B2 level or above Nice to have Experience in conventional machine learning practices, including feature engineering, model training, and evaluation methods Familiarity with knowledge graphs along with fine-tuning approaches Previous background in consulting positions or client-facing capacities