Kubernetes

Platform Engineer

Factspan is a pure play data and analytics services organization. We partner with fortune 500 enterprises to build an
analytics center of excellence, generating insights and solutions from raw data to solve business challenges, make strategic
recommendations and implement new processes that help them succeed. With offices in Seattle, Washington and
Bengaluru, India; we use a global delivery model to service our customers. Our customers include industry leaders from
Retail, Financial Services, Hospitality, and technology sectors.
Role Overview
We are seeking an experienced Platform Engineer to design, develop, maintain and integrate large-scale machine learning
systems from end to end. In this role, you will collaborate with data scientists, analysts, and engineering teams to build
and maintain scalable ML pipelines using cutting-edge public cloud and open-source technologies. You will also focus on
enhancing the reliability, efficiency, and accuracy of ML models through model ops, playing a crucial role in technical
projects and continuously driving innovation.

Responsibilities
• Candidate shall have a robust background in machine learning operations, a deep understanding of software
development best practices, and multiple programming languages, frameworks, and technologies.
• Excellent documentation skill with attention to logic, structure, and clarity.
• Strong hands-on programming skills in Python with multiple design patterns.
• Strong hands-on expertise in Kubernetes and Docker, having ability to build and optimize container images.
• Experience with Azure/GCP cloud platform, machine learning services and best practices.
• Strong problem-solving abilities with a capacity to quickly dive deep into issues and propose effective solutions.
• Ability to manage multiple concurrent tasks and prioritize effectively in fast-paced environments.
• Ability to communicate complex technical topics to diverse audiences.
• Hands-on expertise in SQL.
• Experience in collaborating with data scientist to solve ML model execution related challenges, and able to
suggest different solution architecture for overcoming the challenges.
• Good Understanding of machine learning platforms such as Kubeflow and VertexAI, and frameworks such as
XGBoost, PyTorch and TensorFlow.
• Experience with designing and deploying machine learning models in production.
Qualifications & Experience:

• 8+ years of experience as Python Developer with SQL and deployment experience.
• Experience in collaborating with data scientist to solve ML model execution related challenges, and able to
suggest different solution architecture for overcoming the challenges.
• Experience with machine learning tools among phases in MLOps life cycle such as feature store (FEAST), model
registry, problem optimizer (GUROBI) or real-time model serving.

Preferred Certifications:

• CKA – Certified Kubernetes Administrator
• CKAD – Certified Kubernetes Application Developer

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Senior ML engineer

Factspan Overview:
Factspan is a pure play data and analytics services organization. We partner with fortune 500 enterprises to build an analytics centre of excellence, generating insights and solutions from raw data to solve business challenges, make strategic recommendations and implement new processes that help them succeed. With offices in Seattle, Washington and Bangalore, India; we use a global delivery model to service our customers. Our customers include industry leaders from Retail, Financial Services, Hospitality, and technology sectors.

Responsibilities

➢ Selecting features, building, and optimizing classifiers/regression using machine
learning and deep learning techniques
➢ Proficient in using data analytics tools to perform queries and analyses and for defining
and correlating data, and skilled at utilizing data visualization platforms to organize
and present summarizations, predictive analysis, comparative analysis, dashboards, and
reporting.
➢ Processing, cleansing, and verifying the integrity of data used for analysis.
➢ Performing data mining and analytics to support ongoing continuous risk monitoring
and risk assessments of operational data to recognize patterns and trends, investigate
anomalies, and assess internal control environment.
➢ Utilize data analysis by leveraging various statistical techniques, and predictive
modeling to drive and identify indicators of risk
➢ Drive efficiency by automation of manual processes

More responsibilities in detail:
➢ Excellent understanding of machine learning algorithms, such as Random Forest,
Gradient Boosting, Naive Bayes, SVM, KNN. Good understanding of deep learning
algorithms, such as DNN, CNN, RNN, LSTM, Autoencoders.
➢ Deep Knowledge of ML/AI software and packages such as python: scikit-learn,
TensorFlow and R: CARET, PyTorch.
➢ Proficiency in statistics concepts: sampling theory, descriptive statistics, probability
distributions, statistical tests, dimensionality, reduction, Hypothesis testing, maximum
likelihood estimators, inference, etc.
➢ Expertise in model validation, hyperparameter tuning, and model selection techniques
such as cross validation, leave-one-out, bootstrap.
➢ Proficiency in using query languages such as SQL and spark.
➢ Services, Reporting Service, Power BI, Python, PySpark- Distributed Computing. Machine
Learning, Times Series, Data Mining, Mathematical, Modeling, Probability and Stochastic
Processes

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