Senior AI Engineer

We offer a flexible working policy that supports a healthy balance between personal and professional well-being. This role requires in‑office presence on Tuesdays and Thursdays to collaborate, connect, and learn from peers, while also maintaining the flexibility for meaningful work‑life balance.

• Owning the end‑to‑end ML lifecycle for AI systems from model development and evaluation through to scalable production serving, generative AI document intelligence, and agentic system use cases
• Deploying and optimising ML/AI systems on GPUs and Kubernetes‑based cloud infrastructure, including AKS or equivalent platforms, while balancing trade‑offs across scalability, latency, performance, operational complexity, and cost efficiency
• Designing and implementing production‑ready LLM applications and APIs with monitoring, observability, and integration testing built in
• Applying modern engineering practices for production AI systems, including containerised services, CI/CD pipelines, model and version tracking, and release governance
• Collaborating with product and business stakeholders to translate requirements into viable technical solutions
• Conducting code reviews and providing constructive feedback to team members
• Contributing to best practices and standards for AI engineering across the team

• A Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Data Science, Artificial Intelligence, Statistics, or a related field
• 3+ years of experience in ML/AI engineering or software engineering
• Hands‑on experience building and shipping LLM systems into production
• Deep proficiency in Python and modern AI frameworks, including PyTorch and Hugging Face
• Solid understanding of ML fundamentals including hands‑on experience with both traditional ML and modern generative AI and transformer architectures
• Demonstrated experience fine‑tuning language models and deploying them to production
• Experience with GPU optimisation for training and inference workloads
• Experience with Kubernetes deployment on cloud infrastructure (Azure, AWS, or GCP) to optimise systems for scalability, latency, performance, and cost efficiency
• The ability to work collaboratively across teams, communicate with precision, and take ownership from prototype to production

• Experience with distributed training frameworks (e.g., PyTorch Distributed, Ray) and inference optimisation tools (e.g., vLLM, SGLang)
• Experience with agentic engineering, including agent harness and agent memory, and orchestration frameworks such as Lang Chain, Llama Index, or similar tools
• Experience with knowledge graphs and multimodal LLMs
• Familiarity with AI/ML observability tools and model lifecycle management best practices

• Creating an inclusive environment that encourages shaping culture
• Market‑competitive salary reviewed regularly based on industry data
• Annual performance‑based bonus
• Comprehensive Health/Vision/Dental/Life Insurance
• 401(k) Retirement Savings Plan with company match up to 4%
• Health Joy healthcare concierge service
• Enhanced leave for expecting parents: 20 weeks 100% paid primary leave, 10 weeks 100% paid secondary leave
• Flexible time‑off policy to take time for vacation, volunteering, holidays, family, or recharge
• Multiple company wellness days and free access to the Healthy Minds app for mindfulness and meditation

The overall US annual base salary range for this position is $130,000–$170,000. Individual compensation for each candidate depends on factors such as qualifications, experience, and candidate location. This range does not include bonuses or other benefits.

iManage provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.