Machine Learning Engineer

Pradeepit Global Consulting Private Limited; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Pradeepit Global Consulting Private Limited is seeking a Senior ML Engineer to lead end-to-end ML lifecycle initiatives. You will acquire data, clean and engineer features, train and deploy models across NLP, timeseries, and vision tasks, and optimize pipelines for production readiness.

You will translate business goals into measurable ML solutions, validate approaches, and collaborate with data scientists, data engineers, and architects to ensure scalable, reliable systems in on-prem, cloud, or

Qualifications

• 4-7 years of experience in Data Science and 3+ years as ML Engineer.
• Rich hands-on experience of 5+ years in writing object-oriented code using Python.
• Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
• In-depth knowledge of mathematics, statistics and algorithms.
• Experience with ML frameworks like TensorFlow, Caffe, etc.
• Understanding of Data Structures, Data Systems and software architecture.
• Experience in building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
• Experience with Azure cloud services, Cosmos DB, Streaming Analytics, IoT messaging capacity, Azure functions, Azure compute environments, etc.
• Exposure to deep learning approaches and modeling frameworks (PyTorch, TensorFlow, Keras, etc)
• Experience Level Senior Level

Responsibilities

• Work on end-to-end ML Lifecycle from acquiring data, data cleaning, model building and deployment of models
• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
• Verifying data quality, and/or ensuring it via data cleaning
• Experience in building Machine Learning and Deep Learning models either with predictive algorithms, Timeseries, NLP or Computer Vision and deployment of the same
• Anaylzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
• Supervising the data acquisition process if more data is needed
• Finding available datasets online that could be used for training and data augmentation pipelines
• Defining validation strategies, defining preprocessing or feature engineering to be done on a given dataset
• Training models and tuning their hyperparameters
• Analyzing the errors of the model and designing strategies to overcome them
• Deploying models to production
• Design/architect state-of-the-art data pipelines - on-prem, cloud and hybrid.
• Automate and streamline existing processes, procedures, and toolsets
• Innovate new ways of managing, transforming and validating data
• Ensure code paths are unit tested, defect free and integration tested
• Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
• Design and implement cloud solutions, build MLOps on Azure
• Work with workflow orchestration tools like Kubeflow, Airflow, Argo or similar tools
• Data science models testing, validation and tests automation.
• Communicate with a team of data scientists, data engineers and architect, document the processes.

Skills

Python

ML frameworks

Deep learning

Mathematics & statistics

Tools

Docker

Kubernetes

Azure cloud services

Job description

Sr. ML Engineer Job Description

Total Exp in years - 4 7

• Work on end-to-end ML Lifecycle from acquiring data, data cleaning, model building and deployment of models
• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
• Verifying data quality, and/or ensuring it via data cleaning
• Experience in building Machine Learning and Deep Learning models either with predictive algorithms, Timeseries, NLP or Computer Vision and deployment of the same
• Anaylzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
• Supervising the data acquisition process if more data is needed
• Finding available datasets online that could be used for training and data augmentation pipelines
• Defining validation strategies, defining preprocessing or feature engineering to be done on a given dataset
• Training models and tuning their hyperparameters
• Analyzing the errors of the model and designing strategies to overcome them
• Deploying models to production
• Design/architect state-of-the-art data pipelines - on-prem, cloud and hybrid.
• Automate and streamline existing processes, procedures, and toolsets
• Innovate new ways of managing, transforming and validating data
• Ensure code paths are unit tested, defect free and integration tested
• Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
• Design and implement cloud solutions, build MLOps on Azure
• Work with workflow orchestration tools like Kubeflow, Airflow, Argo or similar tools
• Data science models testing, validation and tests automation.
• Communicate with a team of data scientists, data engineers and architect, document the processes.

Mandatory Skills:

• 4 7 years of experience in Data Science and 3+ years as ML Engineer
• Rich hands-on experience of 5+ years in writing object-oriented code using python
• Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
• In-depth knowledge of mathematics, statistics and algorithms
• Experience working with machine learning frameworks like Tensorflow, Caffe, etc.
• Understanding of Data Structures, Data Systems and software architecture
• Experience in using frameworks for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
• Experience with Azure cloud services, Cosmos DB, Streaming Analytics, IoT messaging capacity, Azure functions, Azure compute environments, etc.
• Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc)
• Experience Level Senior Level