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MLOps Engineer

Remote: 
Full Remote
Contract: 
Salary: 
160 - 160K yearly
Experience: 
Mid-level (2-5 years)
Work from: 

Offer summary

Qualifications:

Bachelor’s degree in data-related field, 3+ years of ML monitoring experience, Fluent in Python, Understanding of DevOps and CI/CD, Attention to detail and documentation skills.

Key responsabilities:

  • Implement monitoring for ML models
  • Collaborate with cross-functional teams
  • Design alerting systems for anomalies
  • Create quality documentation for troubleshooting
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Datasite Large https://www.datasite.com/
1001 - 5000 Employees
See more Datasite offers

Job description

Datasite is where deals are made. We provide the data rooms and SaaS technology used in M&A and other high-value transactions, to deliver projects in more than 170 countries. Carrying that success into the future is all about you. Your useful skills, your unusual experience, your unique ideas. Everyone here brings something unexpected. What’s yours? Invest your talents in us, and we’ll return the compliment.

Job Description:

At Datasite we are transforming the way companies do Mergers and Acquisitions (M&A). We have the industry leading M&A platform: Datasite One that is built on modern micro-services architecture.

We are looking for a talented MLOPs Engineer to implement production monitoring and alerting for a wide range of machine learning models.  As a MLOps Engineer, you will build data pipeline monitoring and create solutions for effectively monitoring outputs of various types of regression, classification, neural network, clustering, and LLM models.

We are open to US remote however we are unable to sponsor or take over sponsorship of an employment Visa at this time.

Essential Duties and Responsibilities:

  • Work with Data Scientists to ensure that models perform as expected during initial Production implementation
  • Collaborate with Data Scientists, ML Engineers, and DevOps Engineers to ensure smooth model integration into Production
  • Design and implement monitoring and alerting for all machine learning model inputs and outputs
  • Demonstrate effectiveness of alerting protocol for detecting anomalous data inputs
  • Create high quality documentation so other teams can understand monitoring rationale and effectively troubleshoot alerts

Qualifications:

  • Bachelor’s degree or equivalent in data science, machine learning, computer science, mathematics, or statistics
  • 3+ years of industry experience successfully implementing monitoring systems for ML models
  • Experience implementing validation and monitoring protocols for machine learning models
  • Fluent in Python
  • Meticulous attention to detail
  • Experience with relational (e.g. Snowflake) and non-relational databases (e.g. MongoDB)
  • Understanding of machine learning approaches and model training processes
  • Understanding of DevOps and CI/CD fundamentals
  • Exposure to LLMs
  • Reputation for creating high quality documentation
  • Self-motivated and able to work both independently and as part of a team
  • Ability to quickly learn, evaluate, and apply emerging technology

The base salary range represents the estimated low and high end for this position at the time of this posting. Consistent with applicable law, each candidate’s compensation offer may vary and will be determined based on but not limited to, your geographic region, skills, qualifications, and experience along with the requirements of the position. Datasite reserves the right to modify this pay range at any time.

Salary range $100-160k

As a global organization, Datasite knows that diverse perspectives are essential to our success. We’re committed to maintaining a diverse workforce to serve our customers around the world. Datasite is an equal opportunity employer (EEO) and furthers the principles of EEO through Affirmative Action.

Required profile

Experience

Level of experience: Mid-level (2-5 years)
Spoken language(s):
English
Check out the description to know which languages are mandatory.

Other Skills

  • Teamwork
  • Detail Oriented
  • Collaboration
  • Self-Motivation

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