Job Location: Thiruvananthapuram
Role Proficiency:
Under guidance from Senior ML Engineers develop ML models that provides accurate results with controls to solve the business problem identified using state of art techniques.
Outcomes:
- Executes relevant data wrangling activities related to the problem in order to create dataset
- Conduct ML experiments to understand the feasibility and build baseline models to solve the business problem
- Fine tune the baseline model for optimum performance
- Test Models internally per acceptance criteria from business
- Document relevant Artefacts for communicating with the business
- Work with data scientists to deploy the models.
- Work with product teams in planning and execution of new product releases.
- Set OKRs and success steps for self/ team and provide feedback to goals for team members
- Work with cross functional teams – business technology and product teams to understand the product vision; building ML solutions that provides value to the product
Measures of Outcomes:
- Selection of the appropriate approach to the problem
- Number of successful deployments of the model with optimised accuracy for baseline model
- Adherence to project schedule / timelines
- Personal and team achievement of quarterly/yearly objectives (OKR Assignments HIG Stretch goals)
Outputs Expected:
Design to deliver Product Objectives:
- Design ML solutions which are aligned to and achieve product objectives
- Define data requirements for the model building and model monitoring; working with product managers to get necessary data
Updated on state of art techniques in the area of AI / ML :
- Perform necessary research using the latest and state of art techniques to design scalable approaches
- Explain the relevance of the technologies
its pros and cons to the product team; enabling accurate design experiences
Skill Examples:
- Technically strong with the ability to connect the dots
- Ability to communicate the relevance of technology to the stakeholders in a simple relatable language
- Curiosity to learn more about new business domains and Technology Innovation
- An empathetic listener who can give and receive honest thoughtful feedback
Knowledge Examples:
- Expertise in machine learning model building lifecycle
- Clear understanding of various ML techniques with appropriate use to business problems
- A strong background of statistics and Mathematics
- Expertise in one of the domains – Computer Vision Language Understanding or structured data
- Experience in executing collaboratively with engineering design user research teams and business stakeholders
- Experience with data wrangling techniques preprocessing and post processing requirements for ML solutions
- Good knowledge python and deep learning frameworks like Tensorflow Pytorch Caffe
- Familiar with the machine learning model testing approaches
- A genuine eagerness to work and learn from a diverse and talented team
Additional Comments:
NACore Job Responsibilities: • Develop end-to-end ML pipelines encompassing the ML lifecycle from data ingestion, data transformation, model training, model validation, model serving, and model evaluation over time. • Collaborate closely with AI scientists to accelerate productionization of ML algorithms. • Setup CI/CD/CT pipelines for ML algorithms. • Deploy models as a service both on-cloud and on-prem. • Learn and apply new tools, technologies, and industry best practices.
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