Primary Skills

• • Integration testing, Value Quantification : Pre-Model Development, Cloud Computing, Value Quantification: Post-Model Deployment, Deployment Strategies (A/B, Blue green, Canary), Great Expectation, Evidently AI, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Model Provisioning: Kubernetes, Kibana, Python/PySpark, R/ R Studio, SAS/SPSS, Tools(KubeFlow, BentoML), Model Experimentation, Model Monitoring, Model testing

Specialization

• • ML Engineering: Data Analyst

Job requirements

• • Design and Development: Design, build, and deploy scalable machine learning models to solve business problems.

  • Data Preparation: Collect, preprocess, and analyze large datasets for training and validation of models.

  • Model Training and Optimization: Train machine learning models, perform hyperparameter tuning, and optimize algorithms for performance.

  • Model Deployment: Implement and deploy machine learning models into production environments, ensuring robustness and scalability.

  • Monitoring and Maintenance: Monitor model performance in production and retrain models as needed to maintain accuracy and efficiency.

  • Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to understand requirements and deliver solutions.

  • Research and Innovation: Stay updated with the latest research and advancements in machine learning and AI, and integrate new techniques into existing systems.

  • Documentation: Create and maintain documentation for models, data pipelines, and other technical processes. Qualifications

  • Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. PhD is a plus.

  • Experience: Proven experience as a Machine Learning Engineer or similar role, with a strong portfolio of deployed machine learning models.

  • Technical Skills: o Proficiency in programming languages such as Python, R, or Java. o Strong understanding of machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and Keras. o Experience with data processing tools like Pandas, NumPy, and SQL. o Knowledge of big data technologies such as Hadoop, Spark, and distributed computing. o Familiarity with cloud platforms like AWS, Google Cloud, or Azure.

  • Analytical Skills: Strong problem-solving skills and the ability to analyze complex datasets.

  • Communication Skills: Excellent verbal and written communication skills, with the ability to convey technical concepts to non-technical stakeholders. • Team Player: Ability to work collaboratively in a fast-paced, team-oriented environment. Preferred Qualifications

  • Experience with MLOps: Knowledge of machine learning operations (MLOps) practices, including continuous integration/continuous deployment (CI/CD) for machine learning.

  • Domain Expertise: Experience in specific domains such as finance, healthcare, or e-commerce.

  • Publications and Patents: Publications in top-tier machine learning conferences or patents related to machine learning technologies.

  • Key Responsibilities