About Moative
Moative, an Applied AI Services company, designs AI roadmaps, builds co-pilots and predictive AI solutions for companies in energy, utilities, packaging, commerce, and other primary industries. Through Moative Labs, we aspire to build micro-products and launch AI startups in vertical markets.
Our Past: We have built and sold two companies, one of which was an AI company. Our founders and leaders are Math PhDs, Ivy League University Alumni, Ex-Googlers, and successful entrepreneurs.
Role
We seek experienced ML/AI professionals with strong backgrounds in computer science, software engineering, or related elds to join our Azure-focused MLOps team. If you’re passionate about deploying complex machine learning models in real-world settings, bridging the gap between research and production, and working on high-impact projects, this role is for you.
Work you’ll do
As an operations engineer, you’ll oversee the entire ML lifecycle on Azure—spanning initial proofs-of-concept to large-scale production deployments. You’ll build and maintain automated training, validation, and deployment pipelines using Azure DevOps, Azure ML, and related services, ensuring models are continuously monitored, optimized for performance, and cost-eective. By integrating MLOps practices such as MLow and CI/CD, you’ll drive rapid iteration and experimentation. In close collaboration with senior ML engineers, data scientists, and domain experts, you’ll deliver robust, production-grade ML solutions that directly impact business outcomes.
Responsibilities
- ML-focused DevOps: Set up robust CI/CD pipelines with a strong emphasis on model versioning, automated testing, and advanced deployment strategies on Azure.
- Monitoring & Maintenance: Track and optimize the performance of deployed models through live metrics, alerts, and iterative improvements.
- Automation: Eliminate repetitive tasks around data preparation, model retraining, and inference by leveraging scripting and infrastructure as code (e.g., Terraform, ARM templates).
- Security & Reliability: Implement best practices for securing ML workows on Azure, including identity/access management, container security, and data encryption.
- Collaboration: Work closely with the data science teams to ensure model performance is within agreed SLAs, both for training and inference.
Skills & Requirements
- 2+ years of hands-on programming experience with Python (PySpark or Scala optional).
- Solid knowledge of Azure cloud services (Azure ML, Azure DevOps, ACI/AKS).
- Practical experience with DevOps concepts: CI/CD, containerization (Docker, Kubernetes), infrastructure as code (Terraform, ARM templates).
- Fundamental understanding of MLOps: MLow or similar frameworks for tracking and versioning.
- Familiarity with machine learning frameworks (TensorFlow, PyTorch, XGBoost) and how to operationalize them in production.
- Broad understanding of data structures and data engineering.
Working at Moative
Moative is a young company, but we believe strongly in thinking long-term, while acting with urgency. Our ethos is rooted in innovation, eiciency and high-quality outcomes. We believe the future of work is AI-augmented and boundary less.
Here are some of our guiding principles:
- Think in decades. Act in hours. As an independent company, our moat is time. While our decisions are for the long-term horizon, our execution will be fast – measured in hours and days, not weeks and months.
- Own the canvas. Throw yourself in to build, x or improve – anything that isn’t done right, irrespective of who did it. Be selsh about improving across the organization – because once the rot sets in, we waste years in surgery and recovery.
- Use data or don’t use data. Use data where you ought to but not as a ‘cover-my-back’ political tool. Be capable of making decisions with partial or limited data. Get better at intuition and pattern-matching. Whichever way you go, be mostly right about it.
- Avoid work about work. Process creeps on purpose, unless we constantly question it. We are deliberate about committing to rituals that take time away from the actual work. We truly believe that a meeting that could be an email, should be an email and you don’t need a person with the highest title to say that loud.
- High revenue per person. We work backwards from this metric. Our default is to automate instead of hiring. We multi-skill our people to own more outcomes than hiring someone who has less to do. We don’t like squatting and hoarding that comes in the form of hiring for growth. High revenue per person comes from high quality work from everyone. We demand it.
If this role and our work is of interest to you, please apply here. We encourage you to apply even if you believe you do not meet all the requirements listed above.
That said, you should demonstrate that you are in the 90th percentile or above. This may mean that you have studied in top-notch institutions, won competitions that are intellectually demanding, built something of your own, or rated as an outstanding performer by your current or previous employers.
The position is based out of Chennai. Our work currently involves significant in-person collaboration and we expect you to work out of our offices in Chennai.
Follow Cutshort