Role Description We are looking for a passionate and skilled AI engineer to lead the AI engineering practice at a team or program level. In this role, you will guide the design and delivery of generative AI solutions, shape the technical roadmap for your team(s), and ensure high standards of engineering excellence. You will provide technical leadership to a small group of engineers, collaborate with product and business partners, and act as a trusted technical consultant for stakeholders. - Lead a cross-functional team of software engineers, data scientists and other specialists delivering GenAI solutions. - Guide the design and delivery of AI-powered systems that are scalable, reliable and production-ready. - Set direction for technical decisions, ensuring AI solutions are optimized for performance, cost and maintainability. - Collaborate closely with product managers, designers and stakeholders to align technical solutions with business goals. - Establish and promote best practices in AI engineering, covering testing, guardrails, responsible AI, monitoring and documentation. - Provide architectural guidance, balancing experimentation with delivery in short, safe cycles. - Review designs and code across the team, ensuring quality and consistency in AI-enabled features. - Set direction for GenAI application optimization to improve accuracy, performance, cost and know when model tuning is required. Qualifications - Strong expertise in Python and modern software engineering practices, including CI/CD, testing, version control and system reliability. - Proven ability to design and integrate end-to-end AI systems, ensuring scalability, maintainability and performance. - Deep experience with GenAI and agentic frameworks, guiding teams in their effective use. - Expertise in building and scaling RAG pipelines and integrating vector databases into production systems. - Experience deploying AI solutions on major cloud platforms, using containers and CI/CD pipelines for reproducibility. - Skilled in LLMOps practices and monitoring production systems with observability tools. - Experience with fine-tuning, model adaptation and advanced use of ML/NLP frameworks. Requirements - Ability to articulate complex technical concepts to non-technical audiences and influence senior decision-makers. - Proven ability to inspire, mentor and develop high-performing engineering teams, fostering a collaborative and inclusive environment. - Demonstrates the ability to anticipate technological trends and proactively shape the technical direction of the team. - Skilled in navigating complex internal and external dynamics, driving consensus and achieving technical and business goals. Benefits - Learning & Development: There is no one-size-fits-all career path at Thoughtworks; your career is supported by interactive tools, numerous development programs and teammates who want to help you grow. - Responsible Use of AI in Recruitment: AI tools support our recruitment team with administrative tasks, but do not screen, assess, rank or make hiring decisions. - EEO: Thoughtworks is an equal-opportunity employer committed to providing equal employment opportunities to all qualified applicants. - US - Work Authorization: Applicants must have work authorization that does not require sponsorship of a visa for employment in the United States. - Accommodations: Thoughtworks is committed to providing reasonable accommodations to qualified applicants with disabilities or sincerely held religious beliefs. - Cancellations: Project scope or availability may shift, or projects may be cancelled during the recruitment and selection process. Company Description Thoughtworks is a dynamic and inclusive community of bright and supportive colleagues who are revolutionizing tech. As a leading technology consultancy, we’re pushing boundaries through our purposeful and impactful work. For 30+ years, we’ve delivered extraordinary impact together with our clients by helping them solve complex business problems with technology as the differentiator.

• Design, build, and validate machine learning models for RF emitter identification — including feature engineering from sensor data, training pipeline development, model evaluation, and iterative refinement based on results • Conduct hands-on exploratory data analysis on RF sensor datasets using Python and Jupyter notebooks — writing and running analytical code, characterizing feature distributions, identifying data quality issues, and producing documented findings • Implement and maintain ML data pipelines — ingesting NDF sensor streams, applying rollup and preprocessing logic, constructing training datasets, and ensuring pipeline correctness on constrained edge hardware with no cloud dependency • Collaborate with the technical lead and Principal AI/ML Engineer to investigate RF sensor data quality, attribution reliability, and feature behavior under contention — writing code to characterize error sources, validate assumptions, and reproduce findings • Produce clear technical documentation of experiments, model configurations, and results — maintaining reproducibility through disciplined versioning, and contributing to monthly status reports and team knowledge sharing

• Design and ship AI agent systems end to end, from early experiments through to production, owning the data access layer, retrieval strategy, and integration with Kota's existing infrastructure including Metabase and internal APIs • Build and own the evaluation framework from scratch, including golden datasets, regression harnesses, and the ability to distinguish retrieval from generation failures; think adversarially about prompt injection, data boundary violations, and silent failure modes in a regulated context • Set the technical direction for AI at Kota, with a seat at the table on agent architecture, and where AI creates the most leverage across the product • Work across the pod and Platform team to surface new AI opportunities, collaborate with the AI Product Lead, and raise the bar on engineering practice through peer review and documentation • Pick up slack and expand the role, this is an early pod with room to shape it; the remit will grow with you

• Define the AI engineering roadmap and architecture standards across the organization • Lead build vs. buy vs. integrate decision-making for AI systems and platforms – and be accountable for articulating how you arrived at those decisions • Evaluate emerging tools, frameworks, and models; provide clear, defensible recommendations to leadership • Serve as the ultimate technical escalation point for complex AI/ML system design challenges • Architect AI solutions for our enterprise platform • Design systems for scale, reliability, and cost-efficiency in production environments • Establish LLMOps practices • Ensure AI systems integrate cleanly with existing product and engineering infrastructure • Partner closely with Product and Engineering leadership to align AI capabilities with business outcomes • Receive operational support to help initiate and coordinate cross-functional engagement • Provide clear guidance on resource allocation • Translate complex technical concepts for non-technical audiences • Champion and introduce AI-assisted coding practices and developer tooling across the engineering organization • Define standards for responsible AI development including guardrails, evaluation frameworks, and security considerations • Drive continuous improvement in how the organization builds and ships AI-powered features