Solution Architect – Python with GenAI

Our growing organization needs a Solution Architect with expertise in Python and Generative AI to engineer and roll out enterprise-level GenAI solutions capable of operating reliably in production. Within our Solution Architecture team, you'll steer technical exploration projects, develop reusable building blocks, and contribute to shaping standards for LLM-powered applications. Submit your application now to join forward-looking GenAI initiatives and help clients realize meaningful, transformative outcomes.

Responsibilities

• Develop end-to-end GenAI architecture solutions, covering RAG approaches, Agents, and Multi-Agent designs, tailored to large enterprise needs
• Build reusable accelerator assets and reference solutions to create consistency across delivery
• Head up technical discovery meetings and give practical, on-the-ground support to engineering teams
• Establish evaluation frameworks and outline standards guiding the creation of LLM-based applications
• Work with multiple teams to confirm that architectural approaches remain scalable and manageable over the long haul
• Engineer microservices systems by drawing on recognized patterns and modern technology frameworks
• Oversee integration efforts tied to cloud infrastructure across platforms such as AWS, Azure, or GCP
• Handle container deployment and orchestration responsibilities via Docker and Kubernetes
• Assess and determine fitting vector database technologies, like Pinecone, Weaviate, or Chroma, to power GenAI solutions
• Roll out LLMOps techniques and monitoring systems to elevate the quality of production releases
• Aid in prompt engineering work and RAG evaluation to strengthen application dependability
• Take part in mentoring programs and share insights across the architecture community
• Maintain rigorous coding practices for Python applications intended for production use
• Guide ongoing efforts to enhance system architecture design and solution scalability

Requirements

• Experience spanning 9 to 14 years in software development, with strong expertise in solution architecture and system design
• Advanced Python capabilities focused on writing code fit for production environments
• Skill set covering microservices architecture, design patterns, FastAPI, Redis, Elasticsearch, and Kafka
• Deep hands-on experience creating GenAI applications, including Agents, MCP, RAG, Agentic RAG, and GraphRAG
• Strong knowledge of LangGraph, LangChain, and other orchestration technologies
• Practical background evaluating LLMs and RAG frameworks, along with handling prompt management tasks
• Proven capability delivering GenAI applications that scale effectively in production
• Direct experience with cloud environments, including AWS, Azure, or GCP
• Familiarity with containerization approaches, particularly Docker and Kubernetes
• Understanding of vector database platforms, such as Pinecone, Weaviate, or Chroma
• Solid comprehension of LLMOps principles and the monitoring tools tied to them
• Strong leadership abilities appropriate for directing engineering teams
• Capability to work in client-facing scenarios while collaborating effectively with teams
• Solid English language skills, both spoken and written, at a B2 level or higher

Nice to have

• Experience with conventional machine learning techniques, including feature engineering, model training, and evaluation
• Familiarity with knowledge graph principles and fine-tuning strategies
• Previous exposure to consulting work or roles centered on direct client interaction