At a glanceSummarised by Seekless from the posting.
Must have7
Strong expertise in machine learning, statistical modeling, and data science
PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a closely related quantitative field
Strong programming skills in Python and experience with relevant ML libraries such as PyTorch, TensorFlow, or scikit-learn
Deep understanding of model evaluation, optimization, experimentation, and algorithm development
Ability to solve complex technical problems and translate research into deployable solutions
6–12 years of relevant experience in machine learning, data science, AI engineering, or applied research
Demonstrated experience delivering innovative ML solutions in research or production environments
Nice to have4
Experience in AI architecture, distributed ML systems, and production-scale AI deployment
Research publications, patents, or contributions to open-source AI projects
Familiarity with generative AI, large language models, and advanced deep learning techniques
Experience with cloud platforms, MLOps, and model monitoring frameworks
Skills
Python
PyTorch
TensorFlow
scikit-learn
This role is for one of Weekday’s clientsSalary range: Rs 6000000 - Rs 8000000 (ie INR 60 - 80 LPA)
Min Experience: 6+ years
Location: Bengaluru, KA, India
JobType: full-time
About the role
We are seeking a highly skilled and research-oriented Principal AI Engineer to lead the development of advanced machine learning solutions and data-driven systems. The ideal candidate will possess strong expertise in machine learning and data science, along with a PhD in a relevant technical discipline and a proven ability to translate complex research into practical, scalable AI applications.
In this role, you will drive technical innovation, establish best practices for model development, and collaborate with engineering, product, and research teams to solve challenging business problems. You will be responsible for evaluating emerging methodologies, designing sophisticated ML solutions, and guiding the development of reliable, high-performance AI systems.
Requirements
Key Responsibilities
1. Machine Learning and Model Development
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Design, develop, and deploy advanced machine learning models for complex real-world problems.
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Apply expertise in supervised and unsupervised learning, deep learning, statistical modeling, and optimization techniques.
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Develop robust model training, validation, testing, and evaluation frameworks.
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Analyze large and complex datasets to identify patterns, generate insights, and improve model performance.
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Optimize models for accuracy, scalability, computational efficiency, and production readiness.
2. Data Science and Research
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Lead data science initiatives involving statistical analysis, predictive modeling, experimentation, and feature engineering.
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Investigate emerging research papers, algorithms, and AI methodologies to identify opportunities for innovation.
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Design and conduct experiments to validate hypotheses and evaluate alternative modeling approaches.
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Translate research findings into practical solutions that deliver measurable business value.
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Establish reproducible experimentation practices and document technical findings.
3. AI Architecture and Engineering
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Design scalable architectures for machine learning pipelines, model-serving infrastructure, and data processing systems.
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Collaborate with software and platform engineering teams to integrate ML models into production applications.
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Establish standards for model versioning, monitoring, testing, deployment, and performance optimization.
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Address challenges related to data quality, model drift, reliability, latency, and scalability.
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Promote responsible AI practices, including model interpretability, fairness, privacy, and security.
4. Technical Leadership and Collaboration
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Provide technical direction and mentorship to machine learning engineers, data scientists, and researchers.
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Collaborate with product managers and business stakeholders to define AI opportunities and technical requirements.
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Evaluate new tools, frameworks, and research developments to guide technology decisions.
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Communicate complex technical concepts and research outcomes clearly to technical and non-technical audiences.
Must-Have Skills
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Strong expertise in machine learning, statistical modeling, and data science.
•
PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a closely related quantitative field.
•
Strong programming skills in Python and experience with relevant ML libraries such as PyTorch, TensorFlow, or scikit-learn.
•
Deep understanding of model evaluation, optimization, experimentation, and algorithm development.
•
Ability to solve complex technical problems and translate research into deployable solutions.
Good-to-Have Skills
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Experience in AI architecture, distributed ML systems, and production-scale AI deployment.
•
Research publications, patents, or contributions to open-source AI projects.
•
Familiarity with generative AI, large language models, and advanced deep learning techniques.
•
Experience with cloud platforms, MLOps, and model monitoring frameworks.
Qualifications and Experience
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6–12 years of relevant experience in machine learning, data science, AI engineering, or applied research.
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A PhD in a relevant technical discipline is required.
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Demonstrated experience delivering innovative ML solutions in research or production environments.