Engineer - ML Ops

at  G42

OVERVIEW:

Looking for an ambitious, passionate and creative-minded Engineer MLOps, to support the development of MLOps at Inception, driving and enabling automated machine learning model deployments and managing the full lifecycle of our ML projects.
The opportunity
To play a key supporting role in shaping the overall MLOps strategy & capabilities at Inception.
Inception is the UAE’s national-scale enabler in AI Research and Development. Partnering with Microsoft’s AI SaaS, we offer domain-specific Agentic AI Orchestrator platforms utilizing reasoning agents for precise and cost-effective services. Our focus includes AI incubation, IP creation, applied AI R&D, and AI investment products. By creating models tailored to specific domains and languages, we ensure superior accuracy and efficiency. Collaborating with top universities and industry giants to drive significant advancements in AI technology within the region.

QUALIFICATIONS:

Skills and attributes for success
The measurement of your success will be based on cost savings, the impact of MLOps on business revenue, the number of projects successfully completed, improvements made to existing solutions and processes, and customer Net Promoter Score (NPS).
To qualify for the role you must have
o Minimum of 3 years experience in setting up and maintaining CI/CD pipelines, preferably in a machine learning context.
o Strong understanding of containerization and orchestration tools such as Docker and Kubernetes.
o Familiarity with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
o Proficient in scripting languages such as Python, Bash, or Shell for automation tasks.
Ideally, you’ll also have
o ML Training data lifecycle.
o Data storage and management engines: NoSQL databases; Search engines e.g. Elasticsearch, SQL databases; Timeseries databases.
o Knowledge of distributed computer systems and web software development
o Outstanding communication and presentation skills
What we look for
If you are a performance-driven, inquisitive mind with the agility to adapt to ambiguity, you will fit right in. You should be eager to explore opportunities to build meaningful collaborations with stakeholders and aspire to create unique customer-centric solutions. Bias for action and a passion to conquer new frontiers in the AI space is at the heart of the Inception community.
What working at Inception offers
Culture: An open, diverse and inclusive environment with a global vision that encourages personal growth and focuses on ground-breaking, industry-first innovations.
Career: Outstanding learning, development & growth opportunities via structured training programs and innovative, high-tech projects.
Work-Life: A hybrid work policy to strike the perfect balance between office and home.
Rewards: A competitive remuneration package with a host of perks including healthcare, education support, leave benefits and more.
If you can confidently demonstrate that you meet the criteria above, please contact us as soon as possible

Reporting to the Lead Engineer - MLOps you will be responsible for building the foundation of our MLOps capabilities, working closely with Data Scientists, Data Engineers and multiple Technology Service departments to manage the end-to-end ML lifecycle including automation of machine learning workflows, code deployments, testing, and data validation processes.
The candidate should be well-versed in using tools like Jenkins, GitLab CI, Azure DevOps, or equivalent, and should be able to integrate these tools with machine learning platforms and data repositories.
o Design, implement, and manage CI/CD pipelines tailored for machine learning workflows.
o Ensure that machine learning models are properly versioned and deployed into production, staging, or testing environments automatically.
o Collaborate with data scientists and software engineers to optimize the automation process for training, validating, and deploying machine learning models.
o Continuously monitor the performance and reliability of CI/CD pipelines and make adjustments as necessary.
o Set up environments and fully implement scalable machine learning operations.
o Continuously monitor, optimize, debug and automate MLOps pipelines for increased quality and efficiency—including pipeline level, module level, and system-level inspection.
o Develop & maintain the infrastructure & tools that facilitate the deployment and monitoring of our ML algorithms in production.
o Keep abreast of the latest technology trends to drive standard methodologies and stay ahead of the curve.
o Document and track all systems, pipelines and best practices.