Lead Machine Learning Engineer

The Opportunity

At Brightflag you will join a team at the forefront of AI in Legal Tech that redefines how legal teams operate every day. Here, we challenge ourselves with ambitious goals, best practice standards and a drive for continuous improvement. 

Brightflag is seeking a specialist Machine Learning (ML) Engineer to bridge the gap between AI innovation and production-ready software. While our Data Scientists excel at application and research, you will be the "Center of Excellence" for deployment, responsible for taking experimental models and hardening them into scalable, high-performance features within the Brightflag app. Making sure that features are delivered and that model output is monitored and maintained over time.

You will have the opportunity to work in a culture that values autonomy and encourages novel and creative solutions to problems - not just the same old model development process. You are passionate about moving projects from Proof of Concept to Production in a scalable repeatable manner.

You are a team player working within the data science team who have over 30 years of experience in shipping AI features, as well as collaborating with our product and engineering teams to implement innovative analytical solutions. You’ll ship a product that delivers  the best value for our customers in line with a clear company strategy with data science innovation as a core pillar of the product. You will be focused on external and internal facing Data Science projects - product facing features to provide insight, value & automation for our customers and models that make our internal processes more efficient and robust.

*This is an individual contributor role and does not involve people management.

 

What You Will Be Doing

• Production Deployment: Own the transition of models from proof-of-concept (POC) to production-grade code.
• Model Optimization: Iterate on existing models to improve performance, scalability, and efficiency in a production environment.
• Engineering Collaboration: Work with engineering squads to ensure AI features are seamlessly integrated into our cloud infrastructure.
• Generative AI Scaling: Help scale Generative AI applications, utilizing frameworks like LangChain or LlamaIndex and implementing patterns like RAG.
• MLOps and Feature Architecture: Design, implement, and maintain robust pipelines,infrastructure and CI/CD workflows to automate model retraining and deployment.
• Monitoring & Drift: Implement systems for monitoring model performance, data drift, and accuracy to ensure high-quality outputs without manual intervention.

 

Skills & Experience

To be successful in the role, you need:

• 5+ years of experience with a primary focus on deploying and maintaining ML models in a SaaS product environment
• MSc or equivalent in AI/machine learning
• Strong programming skills in Python
• Experience deploying machine learning systems to the cloud in particular experience working with AWS machine learning stack as well as some familiarity with CI/CD tools such as Jenkins/CircleCI/AWS Codebuild/Bitbucket pipelines/Github actions. Familiarity with AWS CDK or equivalent frameworks is a big plus.
• Strong experience working with docker and containerised environments is a must ; also some bash scripting experience and linux shell knowledge is advantageous. Experience working in a shared code repository with version control using git
• Good familiarity with core machine learning techniques and metrics across classification , regression, clustering.
• Good skills in NLP, foundational representations like TFIDF matrices and topic modelling as well familiarity with Word2vec, Transformers etc... Experience working with text embeddings for data extraction, classification etc..
• Strong familiarity with generative AI text based applications and frameworks, in particular applying generative ai models to product application development. Knowledge of frameworks such as langchain or llamaindex  may be useful but not essential. Similarly familiarity with patterns like RAG would be beneficial but not essential. Knowledge of MCP would be beneficial.
• Comfortable working with ambiguity in a fast-paced product environment
• A passion for building product-focused sol