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 Required skills
• GCP stack: deep hands-on experience with Vertex AI, Cloud Run, Cloud Functions, Big. Query, 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 Python & Data Structures / Algorithms (DSA) Required 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. Good to have
• 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