Skills

Must have

Bachelor's Degree in a relevant technical field, such as computer science, and 6+ years of post-bachelor's machine learning experience; or Master's degree in a technical field + 5+ years of post-grad
Experience developing machine learning models for supervised, unsupervised, ranking, or other relevant applications of machine learning.
Strong understanding of machine learning approaches and algorithms.
Have a track record in deploying scaled ML systems.
Experience working with machine learning frameworks such as TensorFlow, PyTorch, Spark ML, scikit-learn, or related frameworks.
Strong communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
Experience in cross-functional team alignment and collaborating closely with domain experts, engineers, and product specialists.
Entrepreneurial Mindset: Highly motivated and adaptable with a passion for innovation, problem-solving, and making a meaningful impact in a startup environment. Willingness to take ownership and drive

Lead engineering initiatives aimed at the continuous enhancement of the ML platform and build high-quality models to drive value for users and our company.
Hands-on contributor and overseer of ML workflow to build scalable, robust, distributed infrastructure to support machine learning training, inference, and evaluation.
Evaluate the technical tradeoffs of every decision.
Perform code reviews and ensure exceptional code quality.
Mentor and guide junior engineers, fostering a culture of growth, collaboration, and innovation within the technical team.
Iterate quickly without compromising quality.

Bachelor's Degree in a relevant technical field, such as computer science, and 6+ years of post-bachelor's machine learning experience; or Master's degree in a technical field + 5+ years of post-grad machine learning experience; or PhD in a relevant technical field + 3 years of post-grad machine learning experience.
Experience developing machine learning models for supervised, unsupervised, ranking, or other relevant applications of machine learning.
Strong understanding of machine learning approaches and algorithms.
Have a track record in deploying scaled ML systems.
Experience working with machine learning frameworks such as TensorFlow, PyTorch, Spark ML, scikit-learn, or related frameworks.
Strong communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
Experience in cross-functional team alignment and collaborating closely with domain experts, engineers, and product specialists.
Entrepreneurial Mindset: Highly motivated and adaptable with a passion for innovation, problem-solving, and making a meaningful impact in a startup environment. Willingness to take ownership and drive projects from concept to implementation.

Experience in on-prem ML model deployment and observability.
Experience in building and deploying ML models for real-time time series data like sensor/IoT data.

Sr. Machine Learning Engineer

Compensation

Not disclosed

Location

Chennai

Work setup

Not specified

Seniority

Senior

Employment

Not specified

Experience

3+ years

Bachelor's Degree in a relevant technical field, such as computer science, and 6+ years of post-bachelor's machine learning experience; or Master's degree in a technical field + 5+ years of post-grad
Experience developing machine learning models for supervised, unsupervised, ranking, or other relevant applications of machine learning.
Strong understanding of machine learning approaches and algorithms.
Have a track record in deploying scaled ML systems.
Experience working with machine learning frameworks such as TensorFlow, PyTorch, Spark ML, scikit-learn, or related frameworks.
Strong communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
Experience in cross-functional team alignment and collaborating closely with domain experts, engineers, and product specialists.
Entrepreneurial Mindset: Highly motivated and adaptable with a passion for innovation, problem-solving, and making a meaningful impact in a startup environment. Willingness to take ownership and drive

Lead engineering initiatives aimed at the continuous enhancement of the ML platform and build high-quality models to drive value for users and our company.
Hands-on contributor and overseer of ML workflow to build scalable, robust, distributed infrastructure to support machine learning training, inference, and evaluation.
Evaluate the technical tradeoffs of every decision.
Perform code reviews and ensure exceptional code quality.
Mentor and guide junior engineers, fostering a culture of growth, collaboration, and innovation within the technical team.
Iterate quickly without compromising quality.

Bachelor's Degree in a relevant technical field, such as computer science, and 6+ years of post-bachelor's machine learning experience; or Master's degree in a technical field + 5+ years of post-grad machine learning experience; or PhD in a relevant technical field + 3 years of post-grad machine learning experience.
Experience developing machine learning models for supervised, unsupervised, ranking, or other relevant applications of machine learning.
Strong understanding of machine learning approaches and algorithms.
Have a track record in deploying scaled ML systems.
Experience working with machine learning frameworks such as TensorFlow, PyTorch, Spark ML, scikit-learn, or related frameworks.
Strong communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
Experience in cross-functional team alignment and collaborating closely with domain experts, engineers, and product specialists.
Entrepreneurial Mindset: Highly motivated and adaptable with a passion for innovation, problem-solving, and making a meaningful impact in a startup environment. Willingness to take ownership and drive projects from concept to implementation.

Experience in on-prem ML model deployment and observability.
Experience in building and deploying ML models for real-time time series data like sensor/IoT data.