Principal Machine Learning Engineer Job in Tvs Motors
Principal Machine Learning Engineer
Tvs Motors
4+ weeks ago
- Bengaluru, Bangalore Urban, Karnataka
- Not Disclosed
- Full-time
Job Summary
India with a revenue of over 20,000 crore (US$2.9 billion). It has an annual sale of
more than 3 million units and an annual capacity of over 4 million vehicles. TVS
Motor is also the 2nd largest exporter in India with exports to over 60 Countries. A
member of the TVS Group, it is the largest company of the group in terms of size
and turnover.
2) Central Data & Analytics Team: The Central D&A Team at TVS is a group wide team
of Data Scientists and Engineers supporting the various group companies in their
Data Analytics journey. It is organized as a shared advanced capability hub of Deep
Learning capabilities. Members of this team work on cutting edge problems with the
intend to not only solve for todays problem at TVS Group companies, but also for
creating competitive advantage using Machine learning IP
3) Position: Principal Machine Learning Engineer reporting the Head of Data Science
and Engineering, Central Team.
Key Responsibilities
1) Software Engineering:
a. Design and build Machine Learning(ML) systems using cutting edge
software principles and systems, ML Models & Data Infrastructure.
b. Determine operational feasibility of ML systems by analysing, problem
definition, requirements, solution development, and proposed solutions.
c. Develop ML solutions by gathering Machine Learning needs, conferring
with end customers/Data &Analytics team members, understanding data
infrastructure and architecture, and work processes.
2) Data Science :
a. Able to understand of Machine Learning principles including standard
algorithms for Regression and Classification, Deep Learning constructs
(RNN, CNN, RBMs, Auto Encoders, GANs) and AI systems such as Vision,
Voice to Text, NLP/NLU and Recommender systems
b. Able to understand the determinants of success for a Machine Learning
system, Model accuracy and efficiency, Data requirements, Training and
Test constructs, CI/CD for ML systems
c. Able to build standard ML systems using available ML components provided
by AWS, Azure and GCP.
3) Cloud & Big Data:
a. Work on Cloud Infrastructure(Azure, AWS) to provision data to ML models,
build ML systems on deploy them at scale
b. Build ML systems using Spark libraries such as ML Lib, Spark SQL and be
able to deploy them on clusters/machines, both on Cloud and on-Prem
c. Put in place systems to continuously monitor, evaluate and re-train ML
models in production
4) Data Engineering::
a. Design and Implement ETL pipelines to ingest data into databases from
Transactional systems, streaming sensor data
b. Design and Implement automated schedules for monitoring data pipelines,
data quality and data lineage.
c. Build Data APIs to be used by ML models and also be able to design Data
pipelines for ML systems
5) Build Production level Models @ scale
a. Design and implement the machine learning lifecycle at scale, from building
the data infrastructure to train/test Machine learning models to their
production environments.
b. Management of production ML workflows ensuring automated CI/CD
capabilities are built into the work flow
c. Design and Implement alerts/dashboards to ensure continuous monitoring
of production models effectiveness ( accuracy, latency, performance etc.,)
Job Requirements
1) Ability Work closely with Data Analysts, Data Scientists and Business Analysts
2) Comfortable to work in cross-functional team and collaborate with peers during
the project lifecycle
3) Open to travel based on the project and teams locations.
Qualifications
BE/B.Tech/BS/MS/PhD in Computer Science or a related field from a Premier institute (
Tier 1 IITs, BITS and Top NITs, Ivy League US Schools)
Experience
1) 8 - 12 years of work experience in Software Engineering with at least 4-5 years
designing and implementing ML platforms for enterprise-grade ML use cases
2) Start-up experience is a plus
Functional Competencies
1) Masters or Ph.D. in computer science, mathematics, or statistics
2) Strong engineering experience working with Java, Python, and R
3) Experience with vision processing, deep neural networks, Gaussian processes,
and reinforcement learning
4) A solid understanding of both probability and statistics
5) A firm understanding of mathematics (including the role of algorithm theory in
machine learning and complex algorithms that are needed to help machines learn
and communicate)
6) Advanced knowledge of software engineering principles and software
development methodologies ( Git, Scrum, CI/CD)
7) Strong analytical and problem solving skills
8) Experience working with large amounts of data in a high throughput environment
9) Experience working with cloud platforms ( Azure, AWS, GCP)
10) Experience working with messaging tools like Kafka, Kinesis, Spark Streaming
11) Extensive knowledge of machine learning evaluation metrics and best practices
Organization & Role
India with a revenue of over 20,000 crore (US$2.9 billion). It has an annual sale of
more than 3 million units and an annual capacity of over 4 million vehicles. TVS
Motor is also the 2nd largest exporter in India with exports to over 60 Countries. A
member of the TVS Group, it is the largest company of the group in terms of size
and turnover.
2) Central Data & Analytics Team: The Central D&A Team at TVS is a group wide team
of Data Scientists and Engineers supporting the various group companies in their
Data Analytics journey. It is organized as a shared advanced capability hub of Deep
Learning capabilities. Members of this team work on cutting edge problems with the
intend to not only solve for todays problem at TVS Group companies, but also for
creating competitive advantage using Machine learning IP
3) Position: Principal Machine Learning Engineer reporting the Head of Data Science
and Engineering, Central Team.
Key Responsibilities
1) Software Engineering:
a. Design and build Machine Learning(ML) systems using cutting edge
software principles and systems, ML Models & Data Infrastructure.
b. Determine operational feasibility of ML systems by analysing, problem
definition, requirements, solution development, and proposed solutions.
c. Develop ML solutions by gathering Machine Learning needs, conferring
with end customers/Data &Analytics team members, understanding data
infrastructure and architecture, and work processes.
2) Data Science :
a. Able to understand of Machine Learning principles including standard
algorithms for Regression and Classification, Deep Learning constructs
(RNN, CNN, RBMs, Auto Encoders, GANs) and AI systems such as Vision,
Voice to Text, NLP/NLU and Recommender systems
b. Able to understand the determinants of success for a Machine Learning
system, Model accuracy and efficiency, Data requirements, Training and
Test constructs, CI/CD for ML systems
c. Able to build standard ML systems using available ML components provided
by AWS, Azure and GCP.
3) Cloud & Big Data:
a. Work on Cloud Infrastructure(Azure, AWS) to provision data to ML models,
build ML systems on deploy them at scale
b. Build ML systems using Spark libraries such as ML Lib, Spark SQL and be
able to deploy them on clusters/machines, both on Cloud and on-Prem
c. Put in place systems to continuously monitor, evaluate and re-train ML
models in production
4) Data Engineering::
a. Design and Implement ETL pipelines to ingest data into databases from
Transactional systems, streaming sensor data
b. Design and Implement automated schedules for monitoring data pipelines,
data quality and data lineage.
c. Build Data APIs to be used by ML models and also be able to design Data
pipelines for ML systems
5) Build Production level Models @ scale
a. Design and implement the machine learning lifecycle at scale, from building
the data infrastructure to train/test Machine learning models to their
production environments.
b. Management of production ML workflows ensuring automated CI/CD
capabilities are built into the work flow
c. Design and Implement alerts/dashboards to ensure continuous monitoring
of production models effectiveness ( accuracy, latency, performance etc.,)
Job Requirements
1) Ability Work closely with Data Analysts, Data Scientists and Business Analysts
2) Comfortable to work in cross-functional team and collaborate with peers during
the project lifecycle
3) Open to travel based on the project and teams locations.
Qualifications
BE/B.Tech/BS/MS/PhD in Computer Science or a related field from a Premier institute (
Tier 1 IITs, BITS and Top NITs, Ivy League US Schools)
Experience
1) 8 - 12 years of work experience in Software Engineering with at least 4-5 years
designing and implementing ML platforms for enterprise-grade ML use cases
2) Start-up experience is a plus
Functional Competencies
1) Masters or Ph.D. in computer science, mathematics, or statistics
2) Strong engineering experience working with Java, Python, and R
3) Experience with vision processing, deep neural networks, Gaussian processes,
and reinforcement learning
4) A solid understanding of both probability and statistics
5) A firm understanding of mathematics (including the role of algorithm theory in
machine learning and complex algorithms that are needed to help machines learn
and communicate)
6) Advanced knowledge of software engineering principles and software
development methodologies ( Git, Scrum, CI/CD)
7) Strong analytical and problem solving skills
8) Experience working with large amounts of data in a high throughput environment
9) Experience working with cloud platforms ( Azure, AWS, GCP)
10) Experience working with messaging tools like Kafka, Kinesis, Spark Streaming
11) Extensive knowledge of machine learning evaluation metrics and best practices
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