Lead Python With AI Engineer

Role Summary

The POD Lead owns end-to-end delivery of AI solutions on GCP for the client. They design the solution, lead a pod of three FDEs, and are the main technical face to the client's business and IT stakeholders.

Key Responsibilities

• Lead the pod team: plan sprints, assign work, review code and unblock the team
• Translate client business problems into technical solution designs and delivery plans
• Architect AI solutions on GCP, including GenAI, LLM and agent-based workflows
• Design voice and document management solutions: speech-to-text, text-to-speech, conversational voice agents, document extraction and classification
• Own production quality: debugging of complex issues, root-cause analysis, monitoring and performance tuning
• Run client workshops, demos, status reviews and steering updates
• Manage scope, timelines, risks and escalations with the client and internal leadership
• Set coding, security and documentation standards for the pod
• Mentor and grow the Senior and Junior FDEs
  
  

• GCP stack: deep hands-on experience with Vertex AI, Cloud Run, Cloud Functions, BigQuery, Cloud Storage, Pub/Sub, IAM and networking basics
• Python: expert level; production APIs (FastAPI or Flask), async processing, testing and packaging
• AI: GenAI and LLMs (Gemini or similar), prompt design, retrieval-augmented generation (RAG), agent frameworks, model evaluation
• Voice and document management: Speech-to-Text, Text-to-Speech, Dialogflow CX or similar; Document AI, OCR and document workflows
• Debugging: proven track record resolving production incidents across cloud services, logs (Cloud Logging) and traces
• Communication and presentability: confident with senior client stakeholders; clear written updates; strong whiteboarding and demo skills
  
  

• 6+ Years in Expert-level Python programming with extensive experience in designing and building scalable enterprise applications.
• Strong expertise in Object-Oriented Design, Design Patterns, Concurrency, Multi-threading, Async Programming, and Microservices Architecture.
• In-depth knowledge of advanced Data Structures including Trees, Graphs, Heaps, Tries, Hash Maps, Distributed Data Structures, and Large-Scale Data Processing techniques.
• Proven experience in designing efficient algorithms and optimizing systems for performance, scalability, and reliability.
• Strong ability to review code, establish engineering best practices, and mentor engineering teams.
• Deep understanding of computational complexity, architecture trade-offs, and software design principles.
• Experience leading technical design discussions and driving engineering excellence across AI/ML and software development teams.
  
  

• Google Cloud Professional certification (Cloud Architect or Machine Learning Engineer)
• CI/CD and infrastructure as code (Cloud Build, Terraform)
• Prior experience in a client-facing or consulting delivery role
• Experience leading teams of 3–6 engineers