MLOps Tech Lead

Work Location: London, UK

Role type: Permanent/Fixed Term/Contracting

Mode of working: Hybrid

If Hybrid, how many days are required in office? 2 days per week

Number of positions: 1

Duration of assignment: 06 months

The Role:

As a Tech Lead, you will play a critical role in designing, building, and maintaining data pipelines and infrastructure that enable the development and deployment of machine learning models and drive engineering excellence. You will collaborate closely with data scientists, lead ML engineers, and software engineers to ensure data is clean, accessible, and optimised for large-scale processing and analysis.

Your responsibilities:

• Data Pipeline Development: Lead the technical direction of projects and ensure the use of best practices to the best quality.
• Data Integration: Lead and provide expertise on integrating data from various sources, ensuring data consistency, integrity, and quality across the entire data lifecycle.
• Infrastructure Management: Provide guidance for junior & mid Data Engineers on best practices when building and managing data infrastructure, including data lakes, warehouses, and distributed processing systems (e.g., PySpark, Hadoop).
• Data Preparation: Collaborate with data scientists to prepare and transform raw data into formats suitable for machine learning, including feature engineering and data augmentation.
• Automation: Implement automation tools and frameworks (CI/CD) to streamline the deployment and monitoring of machine learning models in production.
• Performance Optimisation: Optimise data processing workflows and storage solutions to improve performance and reduce costs.
• Collaboration: Work closely with cross-functional teams, including data science, engineering, and product management, to deliver data solutions that meet business needs.
• Mentorship: Mentor junior and mid-level data engineers and provide technical guidance on best practices and emerging technologies in data engineering and machine learning.
• Knowledge Sharing and Empowerment: Promote a culture of knowledge sharing within the engineering teams by organising regular technical workshops, brown bag sessions, and code reviews.
• Innovation and Continuous Improvement: Foster a collaborative and inclusive team environment that encourages continuous learning and improvement.

Your Profile:

Essential skills/knowledge/experience:

• Knowledge of machine learning frameworks (e.g., PySpark, PyTorch) and model deployment tools (e.g., MLflow, TensorFlow Serving).
• Strong experience with data processing frameworks (e.g., Apache Spark, Flink).
• Expertise in SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Cassandra).
• Hands-on experience with cloud platforms (e.g., AWS, GCP, Azure) and their data services (e.g., Snowflake, S3, BigQuery, Redshift).
• Experience with containerisation and orchestration tools (e.g., Docker, Kubernetes).
• Familiarity with version control systems (e.g., Git) and CI/CD pipelines.

Desirable skills/knowledge/experience:

• Certifications: AWS Certified Big Data Specialty, Google Professional Data Engineer, or equivalent.

Soft Skills:

• Excellent problem-solving and analytical skills.
• Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Ability to work independently and in a team-oriented, collaborative environment.

Leadership and Communication:

• Strong leadership skills with the ability to inspire and guide the team.
• Lead scrum ceremonies as needed (Standup, Planning, and grooming sessions).
• Excellent verbal and written communication skills, with the ability to articulate complex technical concepts.
• Creating a safe and inclusive environment where all team members feel that their input is valued.

Collaborative Attitude:

• Strong team player with a collaborative approach to working with cross-functional teams.
• Open to feedback and willing to provide constructive criticism to others.
• Be available for the team, responding within a reasonable time frame.
• Building a community across the Media Agency.
• Contribute to a positive and inclusive atmosphere within the team.

Knowledge Sharing and Empowerment:

• Commitment to fostering a learning culture within the team and ensuring knowledge transfer across all levels.
• Support and mentor C3s and C4s engineers by providing them opportunities to lead initiatives.

Deliver Through Others:

• Proactively share domain knowledge.
• Find the direction to take the team towards.
• Provide expert support in creating and running spikes.
• Create a good developer community.
• Lead by example on communication, visibility, and presence.