Senior Machine Learning & AI Engineers at Thoughtworks design, build, maintain and test the architecture and infrastructure for managing machine learning applications, generative AI systems, and agentic AI solutions. They are involved in supporting and contributing to the design of end-to-end applications and products that leverage both traditional ML and modern AI capabilities. They are responsible for building core capabilities including technical and functional machine learning systems, LLM-powered applications, and autonomous agent frameworks, being the anchor for functional streams of work and are accountable for timely delivery.<br><br>As a senior machine learning & AI engineer, you will work on the latest tools, frameworks and offerings including foundation models, agent orchestration platforms, and emerging AI paradigms while also being involved in enabling credible and collaborative problem solving to execute on a strategy.<br><br><strong>Job Responsibilities<br><br></strong><ul><li>You will contribute to design and drive the development of robust scalable architectures and infrastructure for deploying and managing machine learning (ML) applications, generative AI systems, and agentic workflows, ensuring high availability, performance and security.</li><li>You will collaborate with data scientists and engineers to translate business needs into effective and efficient ML systems, LLM-powered applications, and intelligent agent solutions.</li><li>You will own the development and maintenance of core functionalities within AI applications, including ML pipelines, model training and deployment, RAG (Retrieval-Augmented Generation) systems, prompt engineering frameworks, and agent orchestration platforms.</li><li>You will design and implement agentic AI systems that can autonomously perform complex tasks, integrate with external tools and APIs, and collaborate in multi-agent environments.</li><li>You will build and optimize generative AI applications leveraging foundation models, including fine-tuning strategies, prompt optimization, and responsible AI guardrails.</li><li>You will drive the functional stream of work by providing technical expertise in both traditional ML and modern AI paradigms, handling team discussions and ensuring timely delivery of assigned tasks.</li><li>You will stay ahead of the curve by actively exploring and implementing the latest tools, frameworks and offerings in the ML/AI landscape, including emerging foundation models, agent frameworks, and AI safety measures.</li><li>You will facilitate collaborative problem solving within the team by actively listening, communicating effectively and mentoring other engineers on both ML fundamentals and cutting-edge AI techniques.</li><li>You will contribute to the development and execution of the team's overall AI strategy, aligning technical capabilities with business objectives while considering AI ethics and governance.</li><li>You will proactively identify and address challenges related to ML systems, generative AI applications, and autonomous agents, proposing solutions and implementing improvements.<br><br><br></li></ul><strong>Job Qualifications<br><br></strong><strong>Technical Skills<br><br></strong><ul><li>You have experience in writing clean, maintainable and testable code, demonstrating attention to refactoring and readability of the code.</li><li>You are proficient in scripting languages such as Python or Shell for automation and task streamlining.</li><li>You have knowledge of distributed systems and scalable architectures to handle large-scale ML applications and high-throughput AI inference workloads.</li><li>You have experience with building, deploying, and maintaining ML systems using relevant ML techniques and platforms, i.e.: Scikit-learn, TensorFlow, MLFlow, Kubeflow, PyTorch.</li><li>You have hands-on experience with Large Language Models (LLMs) and foundation models, including prompt engineering, fine-tuning techniques (LoRA, QLoRA, PEFT), and working with APIs from providers like OpenAI, Anthropic, Google, and open-source alternatives (Llama, Mistral).</li><li>You have experience building RAG systems and working with vector databases (Pinecone, Weaviate, Chroma, Qdrant) and embedding models for semantic search and retrieval.</li><li>You have practical experience with agentic AI frameworks and tools (LangChain, LlamaIndex, AutoGen, CrewAI) for building autonomous systems that can plan, reason, and execute complex workflows.</li><li>You understand multi-agent system design, tool integration, and orchestration patterns for building collaborative AI systems.</li><li>You have experience with building, deploying and maintaining ML/AI systems and experience with application of MLOps principles, LLMOps practices, and CI/CD to ML and generative AI.</li><li>You have experience in machine learning engineering and data science, are familiar with key ML concepts, algorithms and frameworks, understand ML model lifecycles, and have knowledge of transformer architectures and attention mechanisms.</li><li>You have experience with designing and operating the infrastructure required to run different types of ML training and serving workloads, including fine-tuning LLMs, managing GPU clusters, and optimizing inference for both traditional ML and generative AI models.</li><li>You have hands-on experience with on-premise and cloud services for building and deploying ML/AI pipelines, i.e.: Azure (Azure OpenAI, Azure ML), AWS (Bedrock, SageMaker), GCP (Vertex AI, Model Garden) or Databricks and associated ML/AI managed services.</li><li>You understand AI safety, alignment, and responsible AI principles, including implementing guardrails, content filtering, and bias mitigation strategies in production AI systems.</li><li>You have experience with evaluation frameworks for generative AI and agentic systems, including automated testing of LLM outputs, agent behavior validation, and performance benchmarking.<br><br><br></li></ul><strong>Professional Skills<br><br></strong><ul><li>You understand the importance of stakeholder management and can easily liaise between clients and other key stakeholders throughout projects, effectively communicating complex AI concepts and managing expectations around AI capabilities and limitations.</li><li>You are resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives, especially when working with rapidly evolving AI technologies.</li><li>You don't shy away from risks or conflicts, instead you take them on and skillfully manage them, particularly around AI ethics, governance, and responsible deployment.</li><li>You are eager to coach, mentor and motivate others and you aspire to influence teammates to take positive action and accountability for their work in the AI/ML space.</li><li>You enjoy influencing others and always advocate for technical excellence while being open to change when needed, especially given the fast-paced evolution of AI capabilities.<br><br><br></li></ul><strong>Other Things To Know<br><br></strong><strong>Learning & Development<br><br></strong>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.<br><br><strong>About Thoughtworks<br><br></strong>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.<br><br>See here our AI policy.<br><br>