Data Engineer-Machine Learning

Role overview

Data Engineer-Machine Learning at IBM

Responsibilities

• Develop Machine Learning Solutions: Apply Machine Learning concepts and techniques to address business challenges, interpreting statistical data and identifying relevant features to inform solution development.
• Evaluate Algorithm Performance: Choose appropriate algorithms and evaluate their performance using relevant metrics, ensuring that solutions meet business needs.
• Implement Machine Learning Techniques: Collaborate with stakeholders to implement Machine Learning techniques that drive business value, leveraging expertise to inform technical decisions.

Employment details

• India Software Engineering Hybrid Professional Bangalore, IN Bangalore (0063) IBM India Private Limited Algorithms Data Analysis Data Visualization Deep Learning Inform Interpreting Machine Learning Natural Language Processing Technical Communication Decision Making PyTorch Scikit-learn Tensorflow
• Location: Bangalore [India]

Application details

• As a Data Engineer with expertise in Machine Learning, you will apply Machine Learning concepts and techniques to address business challenges.

About the employer

• A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide.
• With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact.

Additional role details

• You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys.
• At IBM Consulting, curiosity fuels success.
• You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results.
• Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
• You will leverage your skills to drive informed decision-making in the organization.
• Your primary responsibilities will include:
• Communicate Results: Clearly communicate the results of Machine Learning initiatives to stakeholders, enabling informed decision-making across the organization.
• Exposure to Machine Learning Concepts: Familiarity with applying Machine Learning concepts and techniques to address business challenges, including interpreting statistical data and identifying relevant features.
• Algorithm Evaluation Skills: Experience working with algorithms and evaluating their performance using relevant metrics to ensure solutions meet business needs.
• Data Analysis and Interpretation: Exposure to interpreting statistical data to inform solution development and drive informed decision-making.
• Machine Learning Implementation: Experience working with stakeholders to implement Machine Learning techniques that drive business value.
• Technical Communication: Ability to clearly communicate the results of Machine Learning initiatives to stakeholders, enabling informed decision-making across the organization.
• Advanced Algorithm Knowledge: Exposure to a wide range of algorithms and techniques used in Machine Learning, including deep learning and natural language processing.
• Data Visualization Tools: Experience working with data visualization tools to effectively communicate insights and results to stakeholders.
• Specialized Machine Learning Libraries: Exposure to specialized Machine Learning libraries such as scikit-learn, TensorFlow, or PyTorch.