Job Description

Tech Lead / Architect - Python Applications with GenAI Integration
We are seeking a highly experienced Tech Lead / 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 we are not looking for hands-on AI/ML development, candidates must demonstrate deep architectural understanding of the GenAI stack. A strong hands-on background in developing Python-based applications is essential.
Roles and Responsibilities

• Lead the end-to-end architecture and design of Python-based applications integrated with GenAI capabilities.
• Translate business and product requirements into modular, scalable, and maintainable technical solutions.
• Guide and mentor backend, infrastructure, and MLOps teams in implementing architecture blueprints.
• Evaluate and recommend the right mix of tools, frameworks, and platforms - including LLM providers, vector databases, and cloud-based ML services.
• Ensure the security, scalability, and performance of all deployed systems.
• Stay current with advancements in GenAI, and proactively bring relevant capabilities into solution design.

Skills and Qualifications:

• 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 Expertise:

• 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.
• Direct data science work isn't required, but a solid understanding of AI/ML engineering principles and pipeline design is essential to ensure the architecture supports model and data requirements.

Skills Required