Tech Lead

• Bachelor's degree or equivalent practical experience.
• 11 years of professional software development in Python, including 5 years of architecture across multiple systems.
• 3 years of experience owning engineering standards adopted beyond your own team, and mentoring senior engineers.
• Experience working in globally distributed teams across time zones, with engineering, product and business stakeholders in other regions.
• Language and craft. You are an expert in Python. You write idiomatic, fully typed Python, understand asyncio and the GIL in depth, and can diagnose concurrency, CPU and memory problems in production. You set our Python version strategy, dependency policy, framework choices (FastAPI or Django) and coding patterns, and you are a practitioner in at least two other crafts.
• APIs and data formats. You design REST APIs with OpenAPI from first principles: resource modelling, HTTP semantics, pagination, consistent error formats, versioning, idempotency and rate limiting. You know when to use JSON and when to use Protocol Buffers, and how to evolve both without breaking consumers. You set our API and serialisation standards and decide on exceptions.
• Data. You are fluent in PostgreSQL, including schema design, indexing, query plans, transactions and zero-downtime migrations with SQLAlchemy and Alembic. You design DynamoDB and MongoDB models around access patterns, use Redis for caching, and choose the right store for each workload. You own our data-architecture standards, including how datastores are introduced and retired.
• Asynchronous systems and reliability. You design event-driven systems on queues and streams such as SQS, Kafka and Celery or Arq, using retries, dead-letter queues and idempotent consumers so that at least-once delivery is safe. You know the outbox and saga patterns and when orchestration is better than choreography. You own our reference architecture for reliability.
• Cloud and platform. You have built and run production services on AWS or GCP, both as Lambda functions and as Kubernetes workloads, and you understand the trade-offs between them: cold starts, concurrency, cost, capacity and deployment risk. You define infrastructure as code with Terraform or AWS CDK and release safely with canary and blue/green deployments. With Platform Engineering, you decide when we use serverless and when we use Kubernetes, and you set the reference architectures for both.
• Testing and quality. You practise test-driven development and expect every team to do the same. You build balanced test suites: unit tests with pytest, integration tests with Testcontainers, contract tests with Pact, and Playwright end-to-end tests that gate releases. You set our quality standard and approve any exceptions to it.
• Observability and performance. You instrument services with OpenTelemetry and use Datadog and CloudWatch to find problems before users do. You profile with flame graphs, measure p95 and p99 latency, and fix causes rather than symptoms. You set our standards for SLOs, dashboards, alerting and performance budgets, and you lead the hardest incidents to their root cause.
• Security and identity. You write secure code by default and apply the OWASP Top 10 and API Security Top 10 in practice. You design authentication and authorisation with OAuth 2.0 and OIDC, secure service-to-service traffic with mTLS and workload identity, and integrate identity providers such as Okta, Keycloak and Cognito. You set our identity and security standards and own security reviews with the Security team.
• Architecture and design. You design systems that span many services and teams, and you write architecture decision records that make the trade-offs explicit. You own our technical roadmap and style guide, act as the arbiter on standards, and decide on evidence rather than authority. You are able to trace many symptoms back to a single root cause and fix it once.
• Engineering practices. You work trunk-based with GitHub Flow, small changes and feature flags, and you know CI/CD pipelines inside out. You have led deprecations and migrations all the way to switching the old system off. You set our Git, CI/CD and dependency policies, and you make large-scale change routine rather than exceptional.
• AI-first engineering and LLMs. You use Claude Code every day and understand how large language models work: tokens, context windows, cost, structured output, embeddings and retrieval. You have built production features with the Anthropic and OpenAI SDKs, including tool calling and evaluations, and designed agentic services with clear guardrails and human review. You set our AI-first operating model, covering approved models and frameworks, Claude Code conventions, evaluation and release gates, and governance.
• Communication, collaboration and initiative. You communicate clearly and concisely in writing and in conversation, and you adapt your message for engineers, product leaders and executives. You understand complex problems quickly, ask the right questions and confirm shared understanding before acting. You collaborate well across locations and cultures, working asynchronously and handing over cleanly between time zones. You are proactive: you spot problems and opportunities before you are asked, propose solutions and innovations of your own, and drive them through without waiting for instructions.
• Leadership and behaviours. You lead without authority and influence engineering heads and teams across the organisation. You define the hiring bar for each level, calibrate interviewers and give Talent Acquisition clear screening criteria. You mentor senior engineers into technical leaders, build an engineering practice that does not depend on you, and hold the bar under commercial pressure. You say "I don't know" when you don't, and you dig in until you do.

• B.Tech or B.E. in Computer Science, Information Technology or a related field.
• Experience maintaining a widely used open-source library.
• Experience with multi-region and disaster-recovery architecture.
• Experience mapping engineering controls to SOC 2 or ISO 27001.
• Experience with multi-agent systems and agentic frameworks such as LangGraph, Google ADK or the Anthropic Agent SDK.
• Experience running an engineering-productivity measurement programme.
• Experience in TypeScript.

 WPP Open is WPP's AI-enabled marketing operating system, used by brands and agencies around the world. Our engineering teams in India build and run it alongside colleagues in the UK and Europe. As a Principal Engineer you own architecture and engineering standards for Python across teams. You stay hands-on and set the bar for hiring. Our interviews are practical and hands-on.

• →Own the architecture of Python systems across multiple teams, and set the technical roadmap that takes them from where they are today to where the platform needs to be.
• →Stay hands-on: write, review and ship production code, lead by example on test-driven development, profiling and AI-first delivery, and take on the hardest technical problems yourself.
• →Set and maintain our engineering standards for API design, data, reliability, security, observability and testing; publish them as a style guide and reference architectures, certify teams against them, and decide on exceptions.
• →Review designs and architecture decision records across teams, challenge assumptions, and make sure decisions are made on evidence and recorded with their trade-offs.
• →Decide, with Platform Engineering, how we use AWS Lambda and Kubernetes, and own the cost, capacity and reliability consequences of those choices.
• →Lead the response to the most serious production incidents and performance problems, find the root cause and make sure it does not happen again.
• →Drive large-scale change such as migrations, framework upgrades and deprecations across many codebases through to completion, including switching off what has been replaced.
• →Define how we build with AI: the models, SDKs and agentic frameworks we use, how Claude Code is used day to day, and how AI-written code and AI features are evaluated and released.
• →Work with product, engineering and business leaders in other regions to shape what we build, turn business goals into technical plans, and make trade-offs visible.
• →Set the hiring bar for Python engineers at every level, write interview material, calibrate interviewers and give recruiters clear screening criteria.
• →Mentor senior engineers, grow the next generation of technical leaders, and build a community of practice through reviews, talks and knowledge sharing

Who you are: