Principal Forward-Deployed Engineer

We're looking for a Tech Principal to lead technical strategy and execution on our most ambitious product work: lighthouse projects inside Thoughtworks' global Customer Experience, Product and Design team. Here's the shape of it. A client arrives with a big idea and not much time. Within a few days, you've helped them turn it into a concept everyone agrees on. Within a few weeks, there's something real that customers can actually use and react to. Within a quarter, it's live. Then you do it again, somewhere else in the world, with a different team, and you leave both of them better at it than they were before. If you have built a career integrating strategy, architecture, AI engineering, and executive collaboration, this role offers an ideal platform to leverage those skills. The approach you'd be leading: We call it 3/3/3 3 days: Product concept. Everyone aligned on scope, objectives and how we'll measure success. 3 weeks: Working prototype. Desirability, viability and feasibility tested with real users. 3 months: MLP launch. A production-grade minimum lovable product, ready to keep evolving. Think of it as a reference tempo rather than a fixed schedule. The sequence is what we believe in: align quickly, get real evidence early, put something usable in front of customers while the idea is still current. A regulated platform with a heavy integration surface won't run like a greenfield mobile app, and reading the difference is part of the job. AI runs through all of it, and not as a feature we add at the end. We use AI across the whole lifecycle, from specification and design through build, test and operations, and you'd be one of the people defining what good looks like there. Who You'd Work With We work in tight forward-deployed pods: one Product Strategist, one Experience Designer, and two Forward-Deployed Engineers. You're one of the FDEs — the lead engineer on the pod — guiding architecture, delivery cadence, and technical commercial choices, while collaborating with the strategist and designer as equal partners rather than a downstream requirements queue. Some of the best moments in this job happen when team members explore a challenge from complementary perspectives and unlock innovative solutions. On larger programmes you'll lead across multiple pods, each running its own 3/3/3 rhythm — sometimes a mix of Product Innovation, Modernization/Engineering and Data & AI pod types depending on the engagement shape. You'd report to Ahmet Sakar, Director of Technology for CXPD, and operate as part of a small Tech Principal cadre collaborating directly as peers. Lighthouse projects come in all sizes. Sometimes it's one pod and a single product bet. Sometimes it's a multi-year programme with several streams running at once, real platform and modernization work underneath, and a steering committee to keep alongside. Either way, the work is visible, and when it lands it becomes the case study, the reference architecture and the talk you get invited to give. How Autonomous You Are This is a Tech Principal role, not a Tech Lead. Within Thoughtworks' Radar and CXPD's delivery standards, you make the calls: architecture patterns, stack selection, pod composition for your engagements, commercial pricing tolerances within the band, and the technical position in a pitch. Ahmet is the escalation point for the largest bets, cross-region consistency questions, and firm-wide precedent — everything else lands with you. This isn't a role where every decision gets a second look before it lands with the client. Working in KSA This role anchors in Riyadh, working across the Kingdom's most ambitious digital bets — Vision 2030-aligned government transformation, PIF-family portfolio companies, financial services, energy, retail and public sector. Expect regulated-industry delivery, data-residency requirements under PDPL, and a client base where Arabic-language proficiency is an asset, though not a strict requirement. Job responsibilities • Shaping the bet. Running concept sprints with client executives, product managers and designers. Turning a broad ambition into something scoped and testable. Agreeing what success looks like, and what would tell us to stop, before anyone writes code. • Winning the work. Partnering with account leads to shape technical narratives on new pursuits. Supporting the pod construct and 3/3/3 commercial arc with client leaders with a clear understanding of pricing parameters. Owning technical scope in proposals, presenting confidently, and maintaining focus when discovery reveals core project requirements. • Proving it. Setting the technical direction for prototyping: what to build, what to fake, what to buy, and what evidence each prototype needs to produce. Pairing with designers and engineers so prototypes can actually be tested with users. Standing up proper evaluation for anything AI-powered, including golden datasets, offline evals, human review, guardrails, and cost and latency budgets. • Landing it. Owning an architecture that ships in three months and stands up long-term. Establishing delivery systems around it: agentic workflows with human oversight, continuous delivery, test strategy, observability, security, and privacy by design. On larger initiatives, maintaining architectural alignment across streams so each can ship independently. • Advising. Serving as a trusted technology advisor to key stakeholders. Aligning technical options with business outcomes. Presenting to steering committees, facilitating executive workshops, and shaping new opportunities with client partners. • Developing team members. Pairing, reviewing code, and mentoring colleagues. Creating reusable practices to support team continuity. Mentoring engineers onto lighthouse projects and sharing knowledge broadly across internal and external communities. Job Qualifications Technical Skills • Programming languages: Strong proficiency in Python or TypeScript, with working knowledge of the other, alongside experience shipping production code in at least one additional language. • AI & LLM ecosystems: Hands-on experience with major LLM providers (OpenAI, Anthropic, Bedrock, Vertex), RAG and vector databases (pgvector, Pinecone, Elastic), and agent frameworks (LangGraph, LangChain, LlamaIndex). • AI evaluation & observability: Proficiency in offline/online evaluations, golden datasets, LLM-as-judge patterns, tracing, and cost monitoring using tools like Braintrust, LangSmith, Langfuse, or custom harnesses. • AI-assisted development & prototyping: Active adoption of AI tools (Claude Code, Cursor, Copilot, Replit) and rapid prototyping platforms (v0, Lovable, Figma Make) to accelerate delivery. • Cloud platforms & DevOps: Deep expertise in AWS, GCP, or Azure, Infrastructure as Code (Terraform), containerization (Kubernetes, Docker), and automated CI/CD pipelines. • Software architecture & integration: Solid foundation in API design, event-driven streaming (Kafka, Kinesis, Pub/Sub), microservices, and integrating with enterprise legacy systems. • Data engineering & analytics: Experience with modern data platforms (Snowflake, BigQuery, Databricks) and orchestration tools (dbt, Airflow, Dagster) to design architectures for AI workloads. • Frontend dev

Job Qualifications with Technical Skills as listed below. Proficiency in Python or TypeScript (plus working knowledge of the other) and production code in at least one additional language. Hands-on experience with major LLM providers (OpenAI, Anthropic, Bedrock, Vertex), RAG and vector databases (pgvector, Pinecone, Elastic), and agent frameworks (LangGraph, LangChain, LlamaIndex). Experience with offline/online evaluations, golden datasets, LLM-as-judge patterns, tracing, and cost monitoring using tools like Braintrust, LangSmith, Langfuse, or custom harnesses. Active adoption of AI tools (Claude Code, Cursor, Copilot, Replit) and rapid prototyping platforms (v0, Lovable, Figma Make). Deep expertise in AWS, GCP, or Azure; Infrastructure as Code (Terraform); containerization (Kubernetes, Docker); and automated CI/CD pipelines. Solid foundation in API design, event-driven streaming (Kafka, Kinesis, Pub/Sub), microservices, and integrating with enterprise legacy systems. Experience with modern data platforms (Snowflake, BigQuery, Databricks) and orchestration tools (dbt, Airflow, Dagster) for AI workloads.