Our expanding team is on the lookout for a Solution Architect focused on Python and Generative AI, responsible for engineering and delivering enterprise-caliber GenAI solutions built to perform in live production settings. As a member of our Solution Architecture team, you'll direct technical research initiatives, produce reusable tools, and help define standards for applications built around LLMs. Send in your application now to be part of pioneering GenAI work and support clients in reaching game-changing results.

Responsibilities

Design full-scale GenAI architecture solutions, including RAG models, Agents, and Multi-Agent frameworks, built for large-scale enterprise use
Produce reusable accelerator components and reference architectures to bring uniformity to solution delivery
Lead technical discovery sessions and deliver direct, practical support to engineering teams
Build evaluation frameworks and define standards that shape how LLM applications are developed
Collaborate with a range of teams to ensure architectural designs scale appropriately and remain sustainable long-term
Develop microservices-based systems by applying established patterns and current-generation frameworks
Guide the integration of cloud infrastructure elements across environments like AWS, Azure, or GCP
Manage container-based deployment and orchestration efforts through Docker and Kubernetes
Evaluate and pick suitable vector database options, such as Pinecone, Weaviate, or Chroma, for supporting GenAI projects
Implement LLMOps strategies and monitoring tools to strengthen the quality of production rollouts
Support prompt engineering and RAG evaluation activities aimed at improving application reliability
Participate in mentoring initiatives and contribute knowledge across the architecture function
Enforce strong coding standards for production-level Python development
Lead efforts toward ongoing refinement of system architecture and solution scalability

Requirements

A range of 9 to 14 years working in software development, with solid grounding in solution architecture and system design
Expert-level Python skills geared toward delivering code suitable for production
Capability across microservices design, architectural patterns, FastAPI, Redis, Elasticsearch, and Kafka
Extensive hands-on background in GenAI application development, spanning Agents, MCP, RAG, Agentic RAG, and GraphRAG
Deep familiarity with LangGraph, LangChain, and similar orchestration platforms
Applied experience assessing LLMs and RAG systems while overseeing prompt management
Established success delivering GenAI applications that scale well in production settings
Hands-on background using cloud platforms like AWS, Azure, or GCP
Comfort working with containerization technologies, specifically Docker and Kubernetes
Awareness of vector database solutions, including Pinecone, Weaviate, or Chroma
Firm grasp of LLMOps fundamentals and the monitoring tools that support them
Demonstrated leadership skill suited for steering engineering teams
Comfortable operating in client-facing capacities while maintaining strong team collaboration
Strong English skills, covering both writing and speaking, at B2 level or above

Nice to have

Background in traditional machine learning work, covering feature engineering, model training, and evaluation practices
Understanding of knowledge graph concepts along with fine-tuning techniques
Prior experience in consulting roles or positions involving direct client engagement

Solution Architect Python