Gramener
Website:
gramener.com
Company:
https://www.linkedin.com/company/gramener
Seniority: Mid-Senior level
Industries: Software Development, Data Infrastructure and Analytics, and IT System Data Services
Job details:
Architect – Python Applications with GenAI Integration
We are seeking a highly experienced Architect to drive the architecture and delivery of modern, scalable data applications powered by Generative AI (GenAI). This role focuses on solution design, architectural leadership, and cross-functional collaboration across AI/ML platforms, backend engineering, and cloud-native infrastructure.
While hands-on AI/ML development is not expected, candidates must demonstrate deep architectural understanding of the GenAI stack. A strong hands-on background in Python-based application development is essential.
Required Experience:
- 8+ years of experience in software engineering, with a minimum of 4 years in a technical leadership or architecture role.
- Proven track record in building robust Python-based backend systems, using frameworks such as FastAPI, Flask, or Django.
- Expertise in microservices architecture, distributed system design, and integration patterns.
- Strong familiarity with cloud platforms such as AWS, Azure, or GCP, and practical knowledge of AI/ML services like SageMaker, Vertex AI, or Azure ML.
- Architectural experience with vector databases (e.g., Pinecone, FAISS, Weaviate, Qdrant) for semantic search and RAG pipelines.
- Understanding of DevOps practices, including Docker, Kubernetes, and infrastructure as code.
- Knowledge of CI/CD processes, system security, and observability/monitoring frameworks.
GenAI & AI/ML Architecture Knowledge:
- Sound understanding of GenAI system components, including LLM lifecycles, embedding generation, prompt orchestration, and RAG architectures.
- Ability to architect complete GenAI workflows, from context ingestion and enrichment to inference handling and post-processing.
- Familiarity with API orchestration, agent-based design patterns, and semantic search strategies.
- While direct data science work is not required, a strong grasp of AI/ML engineering principles and pipeline design is expected to ensure architecture aligns with model and data needs.
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