Technical Architect

Tech Lead / Architect – Python Applications with GenAI Integration:

Tech Lead / Architect

deep architectural understanding

Key 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.

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 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.
• 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.