About the role
Experience: 6+ years · Positions: 1
· Reports to: Delivery / Engagement
Head
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.
•Lead the pod (1 Senior FDE, 2
Junior FDEs): 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
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.
•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