Lead AI Engineer

Remote from: Australia
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Software Engineering
Employment type: Full Time,
Job posted: 5 Jun 2026
Apply before: 5 Jul 2026
Experience level: Senior
Views / Applies: 43 / 5

Software design and delivery, together.

AI Summary

Thoughtworks is seeking a Lead AI Engineer to guide the AI engineering practice at a team or program level. The role involves leading a cross-functional team to deliver generative AI solutions, setting technical roadmaps, and ensuring engineering excellence. Responsibilities include designing scalable AI systems, establishing best practices, and collaborating with product and business partners. Candidates need strong expertise in Python, GenAI, RAG pipelines, LLMOps, and cloud platforms, plus leadership and communication skills. This is an opportunity to work at the forefront of AI innovation with a focus on responsible and production-ready solutions.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight This role requires advanced technical expertise in multiple AI domains and the ability to lead and mentor teams, making it highly challenging but not the hardest as it is a lead position, not a principal or architect role.

Salary Analysis

AI Insight The salary for this role is not explicitly listed, but based on market data for a Lead AI Engineer in the US, the typical range is $150,000 to $220,000 per year. Thoughtworks offers competitive compensation and a strong focus on growth and culture, so the actual offer likely falls within this range.

Key Skills

Python Generative AI RAG LLMOps Cloud Platforms CI/CD Leadership Cross-functional Collaboration AI Architecture Agentic Frameworks

Cover Letter Sample

Dear Hiring Manager,

I am writing to express my interest in the Lead AI Engineer position at Thoughtworks. With extensive experience in designing and deploying generative AI solutions, I am excited about the opportunity to lead your AI engineering practice. My background in Python, cloud platforms, and productionizing RAG systems aligns perfectly with the requirements.

I have a proven track record of leading cross-functional teams and setting technical direction, ensuring scalable and reliable AI solutions. At my previous role, I successfully delivered an AI-powered platform that improved customer engagement by 30% while maintaining high standards of engineering excellence.

I am particularly drawn to Thoughtworks' commitment to responsible AI and continuous learning. I look forward to the possibility of contributing to your innovative projects and mentoring your talented team.

Thank you for your consideration. I look forward to discussing how I can add value to Thoughtworks.

Sincerely, [Your Name]

Possible Interview Questions

Can you describe a time you led a team to deliver a complex AI solution? What challenges did you face and how did you overcome them?

In my previous role, I led a team to build a generative AI chatbot for customer support. We faced challenges with model hallucinations and latency. I implemented a RAG pipeline with a vector database and fine-tuned the model using domain-specific data. We also set up monitoring with observability tools to track performance and iteratively improved accuracy.

How do you ensure responsible AI practices in your projects?

I incorporate fairness and bias testing into the development lifecycle. For example, we use diverse datasets and audit model outputs for harmful biases. I also advocate for transparent documentation, explainability, and human-in-the-loop validation for high-stakes decisions.

Explain your approach to scaling an AI system from prototype to production.

I start with a well-defined architecture using containers and orchestration for scalability. I implement CI/CD pipelines for automated testing and deployment. I also focus on cost optimization by selecting appropriate model sizes and cloud services, and I set up monitoring for performance and drift detection.

How do you handle disagreements with product managers or stakeholders regarding technical decisions?

I believe in data-driven discussions. I present trade-offs, such as cost vs. accuracy, with concrete examples and prototypes. I listen to their business perspective and find a compromise that aligns with both technical excellence and business goals. For instance, I might suggest an MVP with a simpler model and iterate based on feedback.

What is your experience with LLMOps and monitoring production AI systems?

I have used tools like MLflow, LangFuse, and Prometheus for tracking experiments and monitoring live systems. I set up alerts for latency, error rates, and model degradation. I also implement guardrails to filter out unsafe inputs and outputs, ensuring the system remains robust and compliant.

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.

Job responsibilities

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, while uplifting the team by sharing knowledge, tools and practices that enable both SMEs and generalists to contribute effectively.
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.

Job qualifications

Technical Skills

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.

Professional Skills

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.

Other things to know

Learning & Development

There is no one-size-fits-all career path at Thoughtworks: however you want to develop your career is entirely up to you. But we also balance autonomy with the strength of our cultivation culture. This means your career is supported by interactive tools, numerous development programs and teammates who want to help you grow. We see value in helping each other be our best and that extends to empowering our employees in their career journeys.

Responsible Use of AI in Recruitment

At Thoughtworks, we use AI tools to support our recruitment team with administrative tasks such as drafting communications, scheduling interviews and writing job descriptions.
Crucially, our AI tools do not screen, assess, rank or make hiring decisions. Every application is reviewed by our team and all selection decisions are made exclusively by our interviewers and hiring managers.
We are committed to fairness and responsible AI. We actively manage our AI systems by testing, monitoring for biased outcomes and implementing mitigation measures. We hold our third-party vendors to these same high standards through a rigorous governance process. For additional information, please see our full Thoughtworks AI Policy for Recruitment.

About Thoughtworks

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. Bring your brilliant expertise and commitment for continuous learning to Thoughtworks. Together, let’s be extraordinary.

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See here our AI policy.

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