Sr. ML Engineer

PradeepIt Global Consulting Pvt Ltd.; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

PradeepIT Consulting Services Pvt Ltd in Bengaluru is seeking an experienced ML Engineer/Data Scientist to drive end-to-end ML projects, from data collection and cleaning to model deployment in production. You will translate business objectives into measurable ML solutions and define validation strategies.

You will collaborate with data scientists and engineers to build scalable data pipelines on Azure and on-prem/hybrid environments, applying TensorFlow, PyTorch, and other frameworks while

Qualifications

• 4–7 years of experience in Data Science and 3+ years as ML Engineer
• 5+ years hands-on Python OO coding
• Expertise in ML frameworks and software architecture
• Strong knowledge of maths, statistics and algorithms
• Experience with TensorFlow, Caffe, PyTorch
• Experience building ML pipelines with Docker & Kubernetes
• Azure cloud services and data stores experience

Responsibilities

• Lead end-to-end ML lifecycle from data prep to production deployment
• Develop models aligned with business objectives and metrics
• Validate data quality and design preprocessing/feature engineering
• Design, build, and optimize scalable data pipelines on cloud/on-prem/hybrid
• Collaborate with data scientists, engineers and architects; document processes
• Ensure code quality with unit tests and CI
• Experiment, compare algorithms and tune hyperparameters
• Monitor deployed models and iterate improvements

Skills

Python (OO) programming

Machine learning modeling

Data manipulation

Statistical analysis

Problem solving

Communication

Tools

TensorFlow

PyTorch

Caffe

Docker

Kubernetes

Azure

Job description

• 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
• Analyzing 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)