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, 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

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