Lead ML Engineer

What you’ll do
We’re MiQ, a global programmatic media partner for marketers and agencies. Our people are at the heart of everything we do, so you will be too. No matter the role or the location, we’re all united in the vision to lead the programmatic industry and make it better.

As a Lead - Machine Learning Ops in our data science department, you’ll have the chance to:
● Lead a high performance MLOps team focusing on MLOps governance and making ML applications production ready ● Work with ML Engineers & data engineers to optimize & scale the AI/ML POCs developed by data scientists wrt latency, throughput, cloud cost, maintainability etc. ● Work with MLOps Engineers to design, deploy & monitor end-to-end ML applications ● Mentoring, coaching, providing feedback, building career plans and assessing performance for your direct reports enabling a high performance MLOps team ● Operationalize, grow & support MLOps practice at MiQ with assistance from data engineers, data scientists, devOps specialists. Build MLOps on AWS Cloud ● Lead automation initiatives across components of ML lifecycle following agile development practices - data quality, data processing, model development, testing, model monitoring, ML platform adoption tracking & business value measurement ● Be able to reason, influence stakeholders and drive ML product features from POC to production launch ● Take end-to-end ownership of ML application pipelines in production, and be able to procure cross-functional support as & when needed ● Partner with data products & platforms team to influence their roadmap, so as to enable automated & improved ML governance practices and also evolve our data quality/augmentation/enrichment/harmonization practices ● Be able to hands-on develop high-performance, reliable, testable and maintainable code for ML applications when needed ● Research and evaluate emerging technologies and techniques in the field of AI/ML, data engineering and data visualization to enhance the team’s technical capabilities ● Advocate for software and machine learning engineering best practices within data science team

Who are your stakeholders?

Internal: Engineers/Architects/Data Scientists: While MLOps Lead would sit within the Data Science team, he/she would be working very closely with rest of the tech function i.e. data engineers, software engineers, IT, DevOps & architects to deliver on their day to day tasks
Product Management: MLOps Lead responsibilities would require a lot of interactions with product management for clarifications around the requirements & business processes, to influence data products roadmap etc.
Account Managers / Traders / Sales / Analysts: The ML engineer would be building product features that are used primarily by these internal customers through MiQ proprietary platforms like Lab and Hub. He/She usually won’t directly collaborate with these stakeholders as the market requirements and product feedback is through Product Management team
External: Technology & Data Partners: This role would include a lot of interaction with our cloud & other technology providers to resolve tech issues, explore tech offerings & infrastructure needs, and may include exposure to data partners while working on ensuring data quality across the ML lifecycle

What you’ll bring
• 5+ years of experience with designing and building robust, highly scalable and highly available data driven pipelines and applications
• 3+ years of experience with ML lifecycle management including - model versioning, model and data lineage, model monitoring, model hosting and deployment, scalability, orchestration, continuous training & deployment, and automated pipelines
• 3+ years of hands-on experience in building scalable distributed systems for model training, inferencing (batch & real time) & evaluation of machine learning models
• Strong ability to design end to end ML systems and lead a technical roadmap, work with cross functional teams, with proven capacity to influence and build alignment
• Deep understanding of machine learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection)
• Experience with machine learning models integration with web applications
• Strong DevOps mentality: Knowledge of making a complicated pipeline simple and easy to maintain, with automated governance
• Software engineering fundamentals: version control systems, web services & APIs
• Proficiency in Linux, Docker, Kubernetes, Jenkins, Version control systems
• Good knowledge of machine learning & deep learning architectures, modelling frameworks & libraries (PyTorch, Tensorflow, Keras, scikit-learn etc.)
• Experience with hardware resource management for ML training and/or deployment (CPU/GPU/NPU)
• Experience in using MLOps frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc. and building feature store
• 5+ years of experience programming with Python or Spark or Java with strong understanding of data structures, algorithms & performance complexity
• 5+ experience working with SQL databases, data warehouses & data lakes (MySQL/Hive/Redshift/Big Query etc.)
• 3+ years experience working with cloud infrastructure (preferably AWS)
• Experience with CI/CD systems, agile development processes, test driven development (unit tests & integration tests)
• Excellent data analytical & debugging skills; problem solving, critical thinking & communication skills with strong attention to detail

Good To Have:
• Exposure to NoSQL/Document/Graph databases (such as MongoDB, DynamoDB, Cassandra, Neo4j)
• Experience with ML platforms like SageMaker/Databricks/Azure ML/Vertex AI etc.
• Familiarity with chat-based interfaces, conversational AI, and Generative AI
• A professional ML Engineer certification

We’ve highlighted some key skills, experience and requirements for this role. But please don’t worry if you don’t meet every single one. Our talent team strives to find the best people. They might see something in your background that’s a fit for this role, or another opportunity at MiQ.

If you have a passion for the role, please still apply.

What impact will you create?
In this role, you’ll be embedded inside a vibrant team of data scientists. You'll be leading the MLOps team focusing on - (1) automating governance within the machine learning lifecycle (2) productionization of AI/ML POCs i.e delivering optimized, scalable and high performance end-to-end ML applications.
As MLOps lead, you would be responsible for continuous evolution & maintenance of MiQ's
AI/ML development ecosystem through focus on - operationalizing MLOps practice, tech stack audit & improvements, institutionalizing best practices around ML system design & development, measurement & socialization of business impact created as a result of tech investments. You are an independent thinker who can operate with minimal guidance from product managers and architects. You provide AI thought leadership and will effectively set us up for AI/ML innovation at scale.

What’s in it for you?
Our Center of Excellence is the very heart of MiQ, and it’s where the magic happens. It means everything you do and everything you create will have a huge impact across our entire global business.
MiQ is incredibly proud to foster a welcoming culture. We do everything possible to make sure everyone feels valued for what they bring. With global teams committed to diversity, equity, and inclusion, we’re always moving towards becoming an even better place to work.

Values

Our values are so much more than statements. They unite MiQers in every corner of the world. They shape the way we work and the decisions we make. And they inspire us to stay true to ourselves and to aim for better. Our values are there to be embraced by everyone, so that we naturally live and breathe them. Just like inclusivity, our values flow through everything we do - no matter how big or small.
• We do what we love - Passion
• We figure it out - Determination
• We anticipate the unexpected - Agility
• We always unite - Unite
• We dare to be unconventional - Courage

Benefits
Every region and office have specific perks and benefits, but every person joining MiQ can expect:
• A hybrid work environment
• New hire orientation with job specific onboarding and training
• Internal and global mobility opportunities
• Competitive healthcare benefits
• Bonus and performance incentives
• Generous annual PTO paid parental leave, with two additional paid days to acknowledge holidays, cultural events, or inclusion initiatives.
•Employee resource groups designed to connect people across all MiQ regions, drive action, and support our communities.

Apply today
Equal Opportunity Employer