Machine Learning Ops Engineer

at  Kognitiv Corporation

Role: ML Ops Engineer
Location: London, UK
What We do:
Kognitiv empowers global brands to redefine loyalty through advanced data activation and multi-enterprise collaboration.
Founded in 2008, Kognitiv is challenging brands to redefine how they engage with their customers and deliver meaningful experiences that earn their lifetime loyalty.
In June 2020, Kognitiv and Aimia’s Loyalty Solutions came together to create a data and technology-led business, employing people across 20 countries worldwide. With 200+ clients and partners in more than 50 markets globally, Kognitiv is removing the complexity of cultivating loyalty, data, and partnerships, so brands can deliver enhanced value, personalization, and experiences to today’s consumers, right where they are.
About the opportunity:
The ML Ops Engineer will focus on the deployment, management, and monitoring of machine learning models in production, bridging the gap between data science and operations & will be accountable for collaborating with stakeholders in the Product, Design & Insights teams to innovate, develop, interface with, and optimize our ML Ops infrastructure to provide AI solutions to Kognitiv’s clients.

Areas of key responsibilities:

• Conduct regular technical evaluations of various MLOps products, and quick POCs if needed;
• Improve our existing machine learning systems using your core coding skills and ML knowledge.
• Work with other machine learning engineers to implement algorithms and systems in an efficient way.
• Take end-to-end ownership of machine learning systems - from data pipelines, feature engineering, candidate extraction, model evaluation, model training, as well as integration into our production systems & model monitoring.
• Write scalable code and deploy solutions across cloud computing platforms (Google Cloud Platform (GCP) and Amazon Web Services (AWS)).
• Develop and implement cloud MLOps solutions that support the delivery of machine learning models.
• Collaborate with data scientists and software engineers to build scalable and efficient machine learning pipelines, model training and deployment systems and identify new ML-driven features for Kognitiv’s clients.
• Develop and maintain monitoring and management tools to ensure the reliability and performance of our cloud MLOps infrastructure.
• Work closely with devOps team to debug & maintain our ML infrastructure.
• Work closely with product and design teams to develop intuitive applications.

Required Knowledge & Qualifications:

• A minimum bachelor’s degree in Computer Science, Mathematics/Statistics
• 3+ years of backend machine learning experience, preferably in a large-scale & professional SaaS environment
• Minimum 3-5 years in software development, with a proven track record of delivering production-grade, efficient, and well-structured code.
• Proven experience with writing scalable code & deploying solutions across multiple cloud computing platforms.
• Advanced proficiency in object-oriented programming using Python, with strong skills in libraries such as Pandas and NumPy. Some experience with Polars is preferred.
• Strong understanding of various ML frameworks, including but not limited to XGBoost, Tensorflow, and sklearn.
• Hands-on experience in implementing MLOps practices using tools like Kubeflow and MLflow.
• Robust production environment experience in cloud services: Amazon Web Services (AWS) required; Google Cloud Platform (GCP) preferred
• Knowledgeable in managing containerized applications using Kubernetes and experienced with serverless architecture tools.
• Understanding of Model creation, feature engineering, and model selection.
• Understanding of various MLOps frameworks.
• Experience with Apache Spark and big data streaming infrastructure (data lakes – ADLS Gen 2 or AWS Lake formation, Snowflake, Databricks, S3).
• Experience supporting data stores such as RDMBS (Postgres), KVS (Cassandra / ScyllaDB) and queues / streaming (Kafka).
• Skilled with Terraform, Git, Python, bash / shell scripting, and Docker containers.
• Experienced with CI/CD processes (Jenkins, Ansible) and automated configuration tools (Terraform, Ansible, etc.).
• Experience setting up container orchestration (AWS ECS, Kubernetes / K8s).
• Skilled with dashboard creation and monitoring with tools such as Prometheus and DataDog is preferred.

Skills and abilities:

• Proficiency in deep learning frameworks such as TensorFlow.
• Experience with ML frameworks such as XGBoost.
• Solid understanding of engineering principles and infrastructure best practices.
• Experimental mindset, which will allow you to find & explore different ways to manipulate available data and extract the most meaning.
• Critical, structured thinking paired with curiosity & a learning attitude.
• Ability to adapt to new tools, methods and information to apply to the data set and problem presented.
• Strong understanding of software testing, benchmarking, and continuous integration.
• Exceptional analytical and problem-solving skills.
• A working understanding of LLMs and their MLOps landscape.
• Ability to manipulate, clean, pre-process complex unstructured data for model development.
• The ability to gain fluency in new technologies quickly and work effectively in a dynamic and ever-evolving environment that includes distributed teams and clients.
• Demonstrated success in mentorship in software development, particularly using an Agile process and with large scale SaaS products;
• A diverse base of knowledge that allows you to help your team solve complex technical problems;
• A portfolio of past projects (including notable successes and lessons learned).
• Excellent communication skills and the ability to build high-trust relationships with team members and clients.
• Demonstrated ability to work effectively in team environments and adapt to rapid development cycles.
• Capable of planning out future infrastructure and projecting timelines.
• Ability to work with our highly collaborative team.
• Strong written and verbal communication skills.

We are a passionate and innovative organization looking for exceptional people to come and join us. This is an exciting opportunity to work for an international business, in a high energy environment with a collaborative, smart and passionate team. If you like the sound of us and the role: get in touch!
Here at Kognitiv, we don’t just accept difference - we celebrate it! Greater diversity means greater variation in ways of thinking, perspectives and approach. And we thrive on it for the benefit of our employees and our clients. Kognitiv are committed to creating an inclusive environment and all employment is decided on the basis of qualifications, merit, fit and business need

• Conduct regular technical evaluations of various MLOps products, and quick POCs if needed;
• Improve our existing machine learning systems using your core coding skills and ML knowledge.
• Work with other machine learning engineers to implement algorithms and systems in an efficient way.
• Take end-to-end ownership of machine learning systems - from data pipelines, feature engineering, candidate extraction, model evaluation, model training, as well as integration into our production systems & model monitoring.
• Write scalable code and deploy solutions across cloud computing platforms (Google Cloud Platform (GCP) and Amazon Web Services (AWS)).
• Develop and implement cloud MLOps solutions that support the delivery of machine learning models.
• Collaborate with data scientists and software engineers to build scalable and efficient machine learning pipelines, model training and deployment systems and identify new ML-driven features for Kognitiv’s clients.
• Develop and maintain monitoring and management tools to ensure the reliability and performance of our cloud MLOps infrastructure.
• Work closely with devOps team to debug & maintain our ML infrastructure.
• Work closely with product and design teams to develop intuitive applications