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- Tasks: Lead the development of AI/ML pipelines and frameworks for investment banking.
- Company: Join a prestigious investment bank focused on innovative AI/ML solutions.
- Benefits: Enjoy remote work flexibility and a competitive day rate up to £850.
- Why this job: Be part of a greenfield project shaping the future of capital markets with cutting-edge technology.
- Qualifications: 2+ years in MLOps and 3+ years in AI/ML engineering required.
- Other info: This is a 12-month contract with potential extensions.
We are partnering with a prestigious investment bank to find a highly skilled and hands-on Machine Learning Operations (MLOps) Lead. This role will be pivotal in building out a greenfield framework for the deployment and management of scalable AI/ML solutions, specifically for the front and Middle Office user base.
The role is to define and set up a greenfield standardized MLOps framework for capital markets and set up all the tools and best practices to educate data scientists and equip them with the right tools and expertise. You MUST be hands-on. A strong understanding of DevOps, machine learning, and data engineering is required to enable the right candidate to implement the MLOps processes.
This team are a specialist team and this role in particular is a key position. Once the framework is established, you will become the gatekeeper to lots of other divisions within the bank, who will leverage your knowledge and expertise. As such, you will gain exposure to lots of different business areas and business stakeholders, so relationship building and good communication will be key.
You will bring expertise in data science or data engineering, with a specific focus on MLOps for at least 2 years. This platform is critical and will be rolled out across the bank, so we are looking for only the highest calibre candidates with experience building and being responsible for greenfield MLOps pipelines that handle very large datasets.
You will be responsible for building out a greenfield standardised framework for Capital markets. The core platform is built on Azure Databricks Lakehouse, consolidating data from various front and Middle Office systems to support BI, MI, and advanced AI/ML analytics.
As a lead, you will shape the MLOps framework and establish best practices for deploying and managing AI/ML solutions for a diverse and dynamic user base, including data scientists, quants, risk managers, traders, and other tech-savvy users.
Core Responsibilities:- Lead the development of AI/ML CI/CD pipelines and frameworks for supporting AI/ML and Data Science solutions on Azure Databricks.
- Define and implement best practices for DataOps, DevOps, ModelOps, and LLMOps to standardize and accelerate the AI/ML life cycle.
- Collaborate with Data Scientists and teams across Front Office Quant teams, Sales/Trading desks to build, monitor, and maintain AI/ML solutions.
- Adopt cutting-edge advancements in GenAI and LLM technologies to keep the platform at the forefront of innovation.
- Align with the bank's central Enterprise Advanced Analytics & Artificial Intelligence group to ensure alignment with organizational goals, strategies, and governance.
- Manage large datasets and support data preparation, integration, and analytics across various data sources (orders, quotes, trades, risk, etc.).
- 2+ years of experience in MLOps and at least 3 years in AI/ML engineering.
- Knowledge in Azure Databricks and associated services.
- Proficiency with ML frameworks and libraries in Python.
- Proven experience deploying and maintaining LLM services and solutions.
- Expertise in Azure DevOps and GitHub Actions.
- Familiarity with Databricks CLI and Databricks Job Bundle.
- Strong programming skills in Python and SQL; familiarity with Scala is a plus.
- Solid understanding of AI/ML algorithms, model training, evaluation (including hyperparameter tuning), deployment, monitoring, and governance.
- Experience in handling large datasets and performing data preparation and integration.
- Experience with Agile methodologies and SDLC practices.
- Strong problem-solving, analytical, and communication skills.
- Work on a greenfield project with a major global investment bank.
- Gain deep expertise in MLOps, Azure Databricks, GenAI, and LLM technologies.
- Play a key role in building scalable AI/ML solutions across Capital Markets.
- Remote work flexibility with a competitive day rate.
If you are a talented MLOps professional with the expertise to help build and scale advanced AI/ML solutions in the investment banking space, we'd love to hear from you. Apply now!
How to Apply: If you meet the qualifications and are excited about this opportunity, please submit your CV. We look forward to hearing from you!
Location: Remote (London City- UK based) Very flexible working arrangements
Rate: Up to £850 per day (Outside IR35) or Salary package up to £200k
Job Type: 12-Month Contract (with extensions)
Industry: Investment Banking/Finance Technology
Machine Learning Consultant employer: CipherTek Recruitment
Contact Detail:
CipherTek Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Consultant
✨Tip Number 1
Familiarise yourself with Azure Databricks and its ecosystem. Since this role heavily relies on Azure Databricks Lakehouse, understanding its functionalities and how to leverage them for MLOps will give you a significant edge.
✨Tip Number 2
Network with professionals in the investment banking sector, especially those involved in AI/ML. Building relationships can provide insights into the company culture and expectations, which can be invaluable during interviews.
✨Tip Number 3
Stay updated on the latest trends in MLOps and GenAI technologies. Being knowledgeable about cutting-edge advancements will not only impress your interviewers but also demonstrate your commitment to continuous learning.
✨Tip Number 4
Prepare to discuss your hands-on experience with large datasets and MLOps pipelines. Be ready to share specific examples of challenges you've faced and how you overcame them, as practical experience is crucial for this role.
We think you need these skills to ace Machine Learning Consultant
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in MLOps, AI/ML engineering, and any relevant projects you've worked on. Emphasise your hands-on skills with Azure Databricks and your ability to manage large datasets.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about MLOps and how your background aligns with the role's requirements. Mention specific experiences that demonstrate your expertise in building scalable AI/ML solutions.
Showcase Relevant Skills: Clearly list your technical skills, especially those related to Python, SQL, Azure DevOps, and ML frameworks. Highlight your familiarity with Agile methodologies and your problem-solving abilities, as these are crucial for the role.
Highlight Communication Skills: Since relationship building and communication are key aspects of the role, provide examples of how you've successfully collaborated with cross-functional teams or stakeholders in previous positions.
How to prepare for a job interview at CipherTek Recruitment
✨Showcase Your Hands-On Experience
Make sure to highlight your practical experience with MLOps, especially in building and managing AI/ML pipelines. Be prepared to discuss specific projects where you implemented solutions on Azure Databricks, as this will demonstrate your capability to handle the hands-on aspects of the role.
✨Emphasise Communication Skills
Since relationship building is key in this role, be ready to share examples of how you've effectively communicated with diverse stakeholders. Discuss any experiences where you collaborated with data scientists, quants, or traders, showcasing your ability to bridge technical and non-technical conversations.
✨Demonstrate Your Knowledge of Best Practices
Familiarise yourself with best practices in DataOps, DevOps, and ModelOps. During the interview, articulate how you would implement these practices in a greenfield project, and be prepared to discuss any relevant frameworks or methodologies you've used in the past.
✨Prepare for Technical Questions
Expect technical questions related to AI/ML algorithms, model training, and deployment. Brush up on your knowledge of Python, SQL, and any relevant ML frameworks. Being able to discuss your approach to hyperparameter tuning and monitoring will also set you apart as a strong candidate.
