Technical Architect/Python Architect

Location: Delhi
Job Type: Full Time
Job Mode: 5 days WFO
Total Experience: 12+ Years

Job Description:

Architect Full Stack + Gen AI

Mandatory skills/concepts:

• Python solid experience
• Experience in Python advanced Concepts
• ASYNC Functions
• Data cache
• Minimum exp in JavaScript

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.