Maximize Your Impact with TP Welcome to TP, a general hub of innovation and empowerment, where we redefine the future. With a remarkable €10 billion annual revenue and a global 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.
- 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, implementation plans, assumptions, dependencies, risks, and trade-offs.
- Evaluate build vs. buy vs. partner decisions and coordinate hyperscalers, vendors, startups, and internal teams.
- Ensure a clear transition from pre-sales and design into delivery, including knowledge transfer and ownership definition.
- Embed Responsible AI principles including security, privacy, transparency, explainability, fairness, human oversight, and appropriate use.
- Define guardrails, controls, evaluations, approval criteria, and escalation protocols.
- Identify and mitigate risks related to data, models, prompts, agents, third parties, operations, compliance, and reputation.
- Ensure data segregation, IAM, encryption, secret management, retention, residency, and traceability in multi-client environments.
- Partner with Cybersecurity, Legal, Privacy, Compliance, and Architecture Boards to resolve requirements and exceptions.
- Design how AI solutions are embedded into operational roles, workflows, controls, and metrics.
- Define the appropriate distribution between automation, augmentation, and human handling, including human-in-the-loop and warm transfer.
- Work with Operations, Change Management, Training, and Quality teams to achieve readiness and adoption.
- Establish feedback mechanisms to continuously improve models, prompts, agents, knowledge bases, and processes.
- Measure usage, acceptance, quality, productivity, user experience, and post-deployment benefits.




- Act as the design authority and trusted technical advisor for strategic AI initiatives.
- Mentor architects, engineers, data scientists, product managers, and consultants.
- Promote standards, communities of practice, reusable assets, and continuous improvement.
- Remain hands-on and current with emerging technologies, distinguishing meaningful developments from short-lived trends.
- Contribute to TP talent strategy, candidate assessment, partnerships, and internal capability building.

The Qualifications

- 10+ years of experience in technology, architecture, software engineering, data, digital transformation, or technology consulting.
- 5+ years designing enterprise solutions or leading complex solution architecture.
- 3+ years of applied AI/ML/GenAI experience, with evidence of solutions taken from discovery or prototype into production.
- Client-facing experience leading workshops, executive presentations, technical discovery, and solution decisions.
- Experience working with cross-functional teams, partners, and complex technology environments.
- Hands-on experience with at least one major hyperscaler: AWS, Microsoft Azure, or Google Cloud.
- Bachelor’s degree in Engineering, Computer Science, Information Systems, Data, Mathematics, Business, or a related field.
- A master’s degree or specialization in AI, Data, Architecture, or Transformation is desirable; an MBA is a plus.
- Cloud, AI, or architecture certifications are valued but do not replace evidence of hands-on execution.
- AI and GenAI: LLMs/SLMs, prompt and context engineering, RAG, embeddings, vector databases, agentic workflows, tool use, orchestration, evaluation, and guardrails.
- Architecture:APIs, microservices, event-driven architecture, enterprise integration, IAM, multi-cloud, resilience, observability, and high availability.
- Data:Data platforms, pipelines, lakehouse, data quality, metadata, feature stores, and structured/unstructured data management.
- Industrialization:MLOps/LLMOps, CI/CD, testing, monitoring, model/prompt versioning, incident management, and release governance.
- Hands-on execution: Ability to build prototypes, use notebooks, consume APIs, review Python and SQL, analyze logs, and validate integrations.
- Solution economics: TCO, FinOps, cost per interaction or transaction, capacity, performance, and optimization of model and cloud consumption.

Pre-Employment Screenings By TP policy, employment in this position will be contingent on your successful completion of and passage of a comprehensive background check, including global sanctions and watchlist screening. Important | Policy on Unsolicited Third-Party Candidate Submissions TP does not accept candidate submissions from unsolicited third parties, including recruiters or headhunters. Applications will not be considered, and no contractual association will be established through such submissions.

Culture & Belonging At TP, we are committed to fostering a diverse, equitable, and inclusive workplace. We welcome individuals from all backgrounds and lifestyles and do not discriminate on the basis of gender identity or expression, sexual orientation, race, religion, age, national origin, citizenship, disability, pregnancy status, veteran status, or other differences.

📌 AI Architect (Colombia)
🏢 Tp
📍 Colombia