TECH LEAD AI H/F

The Role

Equipping teams with LLMs and coding agents takes an afternoon. Getting them to work in an AI-Native SDLC, meaning designing and delivering software with agents in the loop rather than alongside, requires evolving the entire surrounding environment: architecture, context management, harness (the system layer that gives an agent its tools, permissions, and execution context), developer platforms, integration into the IS, infrastructure, security, observability, evaluation.

As a Tech Lead, you help our clients understand these transformations, you arbitrate the resulting architectural decisions, and you implement the capabilities they are missing. You design, you advise, and you dive into the code when a decision needs to be tested against reality.

Why we are opening this position

Our clients have moved past the POC stage. Their questions now concern the integration of agents into the IS and the delivery cycle, and what should be shared within a platform rather than left to each team. These are architectural questions, and we don't have anyone whose full-time job it is.

• Understand the client's Product, business, and technological context, their IS, and their delivery environment

• Identify what AI changes and the technological capabilities that are missing to address it

• Design architectures and arbitrate structuring choices: build or buy, platform or team, standardization or autonomy, immediate need or lasting capability

• Prototype or implement certain components when an architectural decision needs to be proven

• Support client teams and upskill Thiga teams

• Intervene in pre-sales as a technical guarantor, challenge requests, and help the client transform a vague problem into a decision

Scaling with coding agents

The initial case. You analyze the existing situation, identify missing capabilities, and design the architecture that allows teams and agents to work together in an observable and secure manner. The real challenge is often knowledge: reconstructing an usable context from repositories, documents, and conventions that no one has maintained for three years.

Deciding what to share

Five teams each experiment with their own models, agents, and tools. You help the client decide what goes into a platform, what remains decentralized, and in what order to proceed. The answer is rarely the grand AI platform. It's more often three well-chosen capabilities that unlock everyone.

Broad Tech Culture

You are first and foremost a good technologist. You understand modern software systems well enough to reason about an entire architecture: software architecture, distributed systems, APIs, data, cloud, infrastructure, security, IAM, CI/CD, developer platforms, observability. We are looking for someone capable of identifying what is structuring, understanding the interactions between layers, and going in-depth when the problem requires it, rather than a specialist in each of these disciplines. You have enough experience, often around ten years, to have seen architectures evolve, reach their limits, and sometimes fail.

True AI Depth

You understand systems built with LLMs and agents, what they enable, and where they get stuck. Context engineering, retrieval, tool use, agentic systems, evals, security, observability: you know how to reason about the architectural properties that these systems introduce and what they imply for the rest of the IS.

Judgment, and Hands-on Skills

You reason in terms of trade-offs, you take a position and explain why one option is preferable in a given context, without confusing architecture with technology accumulation. And you have remained hands-on enough to open code, manipulate APIs, explore infrastructure, or set up a prototype when it's the fastest way to find out.

Product and Client Culture

You seek to understand the problem, the users, the organizational constraints, and the expected outcome before designing a solution. You know how to challenge a client, present several options with their compromises, and then help them decide.

Appreciated

Experience in consulting, Product & Engineering transformations, developer platforms, or AI systems in production. If you write, contribute to open source, or speak at conferences, it's a real plus.

Thiga comes from Product Management, which changes the way AI-Native SDLC is approached. This transformation affects technology, product, and how teams work simultaneously, so we refuse to treat it as a simple tooling or platform issue.

You will be attached to the Tech & AI Tribe, which brings together our Product Builders, the consultants who deploy AI-Native SDLC with our clients, and the architects who build its enablers. Our ambition: to lead real transformations for our clients, build a collective of experts who truly share what they learn, and establish Thiga as a benchmark player in the field.

Therefore, there is a real intrapreneurial dimension, and the freedom that comes with it.

1. Initial discussion with a Recruitment Manager: your background, what you are looking for, and a detailed presentation of Thiga and the role.

2. Case study and defense: we will send you a case study a week in advance. The exercise is not about finding our architecture or reciting a stack; we want to see how you approach a complex problem, how you explore options, how you arbitrate, and how you defend a recommendation. We evaluate four dimensions: Tech depth, AI depth, Product culture, Consulting posture.

3. Final meeting with a Director: delve deeper into what needs to be explored and validate the desire to work together, from both sides.