Overview
As Senior AI Engineer, you will lead the design and deployment of AI-powered features in a SaaS platform focused on legal tech for fund formation and private equity. You’ll own core NLP/ML components, from parsing and search to summarisation, in a production cloud environment. You work closely with engineers, product managers, and customers to ship reliable, scalable AI features. This role offers high-impact work in a lean, fast-moving team with clear ownership and tangible growth impact.
Responsibilities
- Design and implement ML models to improve document parsing, search, classification, and summarisation
- Integrate LLMs into core product workflows with a focus on reliability, performance, and guardrails
- Collaborate with engineers, PMs, QA, and customer-facing teams to deliver AI-driven features
- Optimize model serving and inference pipelines for low latency and cost in AWS (ECS, Lambda, S3, CloudWatch)
- Develop data pipelines for training and evaluation using PostgreSQL, Redis, Elasticsearch/OpenSearch, and S3
- Ensure models meet scalability, security, and compliance requirements for enterprise SaaS deployments
- Stay current on NLP/LLM advancements and contribute innovations to production
- Contribute to documentation, code reviews, and knowledge sharing
Key requirements
- 5+ years of AI/ML engineering experience delivering NLP/ML models into production SaaS
- Strong fundamentals in ML and hands-on NLP with spaCy, Hugging Face Transformers, PyTorch, TensorFlow
- Experience deploying and scaling LLM apps in production (fine-tuning, retrieval-augmented generation, evaluation, monitoring)
- Proficient Python with API development and integration of AI models
- Understanding of distributed systems and AWS (ECS, Lambda, S3, CloudWatch)
- Experience with data pipelines and orchestration for training, evaluation, and monitoring
- Ability to trade off accuracy, cost, performance, and interpretability in applied AI
- English at B2+ level for team communication
- Excellent communicator and collaborator across technical and non-technical teams
- Strong problem-solving with pragmatic, production-ready mindset
- Curious, self-motivated, and committed to continuous learning
- NLP libraries/frameworks (spaCy, Hugging Face Transformers, PyTorch, TensorFlow)
- LLM deployment and evaluation (fine-tuning, retrieval-augmented generation)
- API development and integration of AI models
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