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

Solution Architect Python