Maximize Your Impact with TP

Welcome to TP, a global hub of innovation and empowerment, where we redefine the future. With a remarkable €10 billion annual revenue and a integral team of 500,000 employees serving 170 countries in over 300 languages, we lead in intelligent, digital-first solutions.

As a Great Place to Work certified in 72 countries, our culture thrives on diversity, equity, and inclusion. We value your unique perspective and believe that your talent is the missing piece that completes our vision for a brighter, digitally driven tomorrow.

The Opportunity

The Principal AI Transformation Architect turns strategic business opportunities and operational challenges into implementable, scalable, secure, and measurable Artificial Intelligence solutions for TP and its clients.
The role combines strategic vision, enterprise architecture, and hands‑on technical execution. It contributes from discovery, process redesign, and business case definition through prototyping, target architecture, industrialization, adoption, and value realization.
The role bridges clients, Operations, Product, Consulting, Data, Engineering, Cybersecurity, Legal, Privacy, and technology partners. This is not an exclusively advisory or documentation‑based role: the architect is expected to go deep technically, build or review prototypes, validate integrations, evaluate models and platforms, resolve blockers, and guide teams to a production‑ready solution.

The Responsibilities

- Owns the integrity of the end-to-end solution, from opportunity identification through scaling and operation.
- Acts as the technical and transformation authority for complex AI, GenAI, Agentic AI, advanced analytics, and intelligent automation initiatives.




- Operates as a senior individual contributor who can lead virtual, cross-functional teams without depending on formal people-management authority.
- Balances strategy, architecture, delivery, adoption, risk, economics, and business value.
- Identify internal and client-facing transformation opportunities through analysis of processes, pain points, data, capabilities, and business objectives.
- Lead as-is / to-be assessments and redesign end-to-end processes rather than simply adding AI to existing workflows.
- Build transformation roadmaps and prioritize initiatives based on impact, feasibility, time-to-value, risk, cost, and reuse potential.
- Define baselines, value hypotheses, business cases, TCO, ROI, expected benefits, and success metrics.li>
- Translate AI strategy into an executable portfolio of capabilities, platforms, use cases, and operating-model changes.
- Design end-to-end architectures integrating AI/ML, GenAI, Agentic AI, data, APIs, core systems, channels, workflows, orchestration, and human-in-the-loop mechanisms.
- Define principles, standards, reference architectures, reusable patterns, and Architecture Decision Records.
- Make design decisions covering scalability, resilience, latency, observability, interoperability, security, privacy, and cost.
- Ensure integration across heterogeneous, legacy, multi-cloud,



on-premises, and third-party environments.
- Define selection criteria for models, LLMs/SLMs, components, tools, vector databases, agent platforms, and cloud services.
- Build or directly contribute to PoCs, PoVs, demos, and functional prototypes that validate feasibility and business value.
- Implement or review reference integrations, APIs, RAG pipelines, agent workflows, evaluation methods, and guardrails.
- Read, review, and challenge code; execute technical tests; analyze logs and metrics; diagnose quality, latency, security, or cost issues.
- Benchmark models, platforms, and vendors using structured and reproducible evaluation criteria.
- Create reusable accelerators, blueprints, assets, and components to reduce time-to-market.
- Define the criteria required to move from experimentation to production, including quality, security, resilience, cost, observability, support, and operations.
- Guide Software Engineering, Data Engineering, Data Science, and Platform Engineering teams throughout implementation.
- Lead production-readiness reviews and ensure functional, technical, security, performance, and recovery testing.
- Define continuous evaluation, monitoring, fallback, human escalation, rollback, incident management, and model-improvement mechanisms.
- Remain engaged after go-live to validate adoption, performance, and realized value.
- Lead discovery, co-creation, and solutioning workshops with clients, executives, operations, and technical teams.
- Contribute to RFPs/RFIs, proposals, estimates, demos, executive presentations, and solution defense.
- Translate business needs into architectures, i

📌 AI Architect (Colombia)
🏢 Tp
📍 Colombia