Thoughtworks

Technology

SeniorAIEngineer

$165–225k ~AI est. San Francisco, California, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior AI Engineer at Thoughtworks. Skills: Generative AI, AI solutions, LLMOps. Design GenAI applications. Build GenAI applications”

Industry & Context.

Technology
Problems you'll solve

Complex technical problems

What They're Looking For.

Must Have

Software engineering fundamentals, Python, CI/CD, Testing, Version control, Clean code practices, Solid grasp of software design principles, Design and implement AI-powered components, Experience with at least one GenAI framework, Experience with one agentic framework, Hands-on experience building RAG pipelines, Familiarity with deploying AI solutions, Basic use of containers, Basic use of CI/CD pipelines, Experience using LLMOps tools, Experience using observability tools, Exposure to fine-tuning, Use of ML/NLP frameworks, Ability to independently solve complex problems, Willingness to guide junior engineers, Ability to share knowledge, Ability to share best practices

Nice to Have

Experience with vector databases, Experience with major cloud platforms, Experience with Docker, Experience with PyTorch, Experience with Hugging Face Transformers

What You'll Do.

Design GenAI applications

Build GenAI applications

Deploy GenAI applications

Take ideas from prototype to production

Work with AI/ML engineers

Work with software engineers

Deliver reliable systems

Deliver scalable systems

Ensure well-structured features

Ensure efficient features

Ensure easy to maintain features

Make technical decisions

Contribute to system design

Attention to performance

Attention to reliability

Collaborate with product managers

Collaborate with designers

Collaborate with data scientists

Turn business needs into solutions

Mentor junior team members

Help grow technical skills

Provide constructive feedback

Understand deployment options

Understand trade offs across cloud providers

Understand optimization techniques

How You'll Work.

Team & Collaboration

Cross-functional teams; Diverse teams

Communication Scope

Constructive feedback

Process & Methodology

System design

Full Job Description

We are looking for a passionate and skilled AI engineer who is responsible for designing and delivering complex generative AI solutions. This role requires the ability to work autonomously on challenging technical problems, provide mentorship to junior engineers and champion engineering best practices. Job responsibilities Design, build and deploy GenAI applications using techniques such RAG, Agents, Multi Agent Systems etc., taking ideas from prototype to production. Work with both AI/ML engineers and software engineers to deliver reliable, scalable systems. Own key features or components, ensuring they are well-structured, efficient and easy to maintain. Make technical decisions and contribute to system design with attention to performance, cost and reliability. Collaborate with product managers, designers and data scientists to turn business needs into practical solutions. Share knowledge and mentor junior team members, helping them grow their technical skills. Review code and provide clear, constructive feedback to peers. Understand deployment options and trade offs across different cloud providers such as AWS, Azure, Google Cloud, etc. Understand optimization techniques to improve accuracy, performance and cost for GenAI applications. Job qualifications Technical Skills Strong software engineering fundamentals, including Python, CI/CD, testing, version control and clean code practices. Solid grasp of software design principles and ability to design and implement AI-powered components or workflows. Experience with at least one GenAI framework (e.g., LangChain, LlamaIndex, Semantic Kernel) and one agentic framework (e.g., PydanticAI, LangGraph, AutoGen2). Hands-on experience building and optimizing Retrieval-Augmented Generation (RAG) pipelines with vector databases (e.g., FAISS, Pinecone, Weaviate). Familiarity with deploying AI solutions on major cloud platforms (AWS, Azure or GCP), with basic use of containers (Docker) and CI/CD pipelines. Experience using LLM

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