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About Moative

Moative, an Applied AI company, designs and builds transformation AI solutions for traditional industries in energy, utilities, healthcare & lifesciences, and more. Through Moative Labs, we build AI micro-products and launch AI startups with partners in vertical markets that align with our theses.

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.

Our Team: Our team of 20+ employees consist of data scientists, AI/ML Engineers, and mathematicians from top engineering and research institutes such as IITs, CERN, IISc, UZH, Ph.Ds. Our team includes academicians, IBM Research Fellows, and former founders.

Work you’ll do

As a Data Engineer, you will work on data architecture, large-scale processing systems, and data flow management. You will build and maintain optimal data architecture and data pipelines, assemble large, complex data sets, and ensure that data is readily available to data scientists, analysts, and other users. In close collaboration with ML engineers, data scientists, and domain experts, you’ll deliver robust, production-grade solutions that directly impact business outcomes. Ultimately, you will be responsible for developing and implementing systems that optimize the organization’s data use and data quality.

Responsibilities

• Create and maintain optimal data architecture and data pipelines on cloud infrastructure (such as AWS/ Azure/ GCP)
• Assemble large, complex data sets that meet functional / non-functional business requirements
• Identify, design, and implement internal process improvements
• Build the pipeline infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources
• Support development of analytics that utilize the data pipeline to provide actionable insights into key business metrics
• Work with stakeholders to assist with data-related technical issues and support their data infrastructure needs

Who you are

You are a passionate and results-oriented engineer who understands the importance of data architecture and data quality to impact solution development, enhance products, and ultimately improve business applications. You thrive in dynamic environments and are comfortable navigating ambiguity. You possess a strong sense of ownership and are eager to take initiative, advocating for your technical decisions while remaining open to feedback and collaboration. 

You have experience in developing and deploying data pipelines to support real-world applications. You have a good understanding of data structures and are excellent at writing clean, efficient code to extract, create and manage large data sets for analytical uses. You have the ability to conduct regular testing and debugging to ensure optimal data pipeline performance. You are excited at the possibility of contributing to intelligent applications that can directly impact business services and make a positive difference to users.

Skills & Requirements

• 3+ years of hands-on experience as a data engineer, data architect or similar role, with a good understanding of data structures and data engineering.
• Solid knowledge of cloud infra and data-related services on AWS (EC2, EMR, RDS, Redshift) and/ or Azure.
• Advanced knowledge of SQL, including writing complex queries, stored procedures, views, etc.
• Strong experience with data pipeline and workflow management tools (such as Luigi, Airflow).
• Experience with common relational SQL, NoSQL and Graph databases.
• Strong experience with scripting languages: Python, PySpark, Scala, etc.
• Practical experience with basic DevOps concepts: CI/CD, containerization (Docker, Kubernetes), etc
• Experience with big data tools (Spark, Kafka, etc) and stream processing.
• Excellent communication skills to collaborate with colleagues from both technical and business backgrounds, discuss and convey ideas and findings effectively.
• Ability to analyze complex problems, think critically for troubleshooting and develop robust data solutions.
• Ability to identify and tackle issues efficiently and proactively, conduct thorough research and collaborate to find long-term, scalable solutions.

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, efficiency 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, fix or improve – anything that isn’t done right, irrespective of who did it. Be selfish 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 out 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. 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.

We are looking for an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers. A candidate with an adaptable and productive working style which fits in a fast-moving environment.

Skills:

- 5+ years deploying Machine Learning pipelines in large enterprise production systems.

- Experience developing end to end ML solutions from business hypothesis to deployment / understanding the entirety of the ML development life cycle.
- Expert in modern software development practices; solid experience using source control management (CI/CD).
- Proficient in designing relevant architecture / microservices to fulfil application integration, model monitoring, training / re-training, model management, model deployment, model experimentation/development, alert mechanisms.
- Experience with public cloud platforms (Azure, AWS, GCP).
- Serverless services like lambda, azure functions, and/or cloud functions.
- Orchestration services like data factory, data pipeline, and/or data flow.
- Data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform.
- Data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift.
- Distributed computing services like Pyspark, EMR, Databricks.
- Data storage services like cloud storage, S3, blob, S3 Glacier.
- Data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik.
- Proven experience serving up predictive algorithms and analytics through batch and real-time APIs.
- Solid working experience with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders to design the holistic solution.
- Strong technical acumen around automated testing.
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)
- Strong hands-on experience with statistical packages and ML libraries (e.g., Python scikit learn, Spark MLlib, etc.)
- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.)
- Experience in developing and debugging in one or more of the languages Java, Python.
- Ability to work in cross functional teams.
- Apply Machine Learning techniques in production including, but not limited to, neuralnets, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc.

Roles and Responsibilities:

Deploying ML models into production, and scaling them to serve millions of customers.

Technical solutioning skills with deep understanding of technical API integrations, AI / Data Science, BigData and public cloud architectures / deployments in a SaaS environment.

Strong stakeholder relationship management skills - able to influence and manage the expectations of senior executives.
Strong networking skills with the ability to build and maintain strong relationships with both business, operations and technology teams internally and externally.

Provide software design and programming support to projects.

 Qualifications & Experience:

Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years.