Job ID
66915
Category
Global Data Insight & Analytics
Location
Chennai, India
Work Type
Hybrid
Technical Leadership :
Lead, mentor, a team of data scientists, fostering a collaborative and innovative setting.
Provide technical guidance and oversight throughout the solution development lifecycle.
Drive best practices in MLOps, model development, testing, and deployment.
Hands-on AI ML Development :
Design, build, and deploy end-to-end AI/ML models and systems that address critical after-sales service needs.
Perform data exploration, feature engineering, model training, evaluation, and optimization.
Integrate AI ML solutions with existing enterprise systems and data pipelines.
Strategy & Collaboration :
Collaborate closely with product managers, data scientists, business stakeholders, and IT teams to understand requirements, define project scope, and deliver impactful AI ML solutions.
Translate complex business problems into well-defined AI/ML technical requirements and architectural designs.
Problem Solving :
Identify opportunities to leverage AI ML to improve efficiency, reduce costs, and enhance customer satisfaction in after-sales service.
Debug and troubleshoot complex AI ML systems and data issues.





Doctorate / Bachelor's or Master's degree in Computer Science, Engineering, :Artificial Intelligence, Machine Learning, Data Science, or a related quantitative field.
5+ years of professional experience in Artificial Intelligence, Machine Learning, or Data Science roles.
2+ years of experience leading or mentoring a small team of engineers or data scientists.
Robust hands-on programming proficiency in Python (including libraries like TensorFlow, PyTorch, scikit-learn, pandas, numpy).
Demonstrated experience in designing, building, and deploying machine learning models into production settings.
Experience with Natural Language Processing (NLP)/LLM/Gen AI techniques and tools.
Experience with cloud platforms (e.g., GCP, AWS, Azure) for AI/ML development and deployment.
Experience with MLOps practices and tools (e.g., MLflow, Kubeflow, Docker, Kubernetes).
Knowledge of causal inference, reinforcement learning, or optimization techniques.
Excellent problem-solving skills and the ability to work independently and as part of a team.
Strong communication and interpersonal skills, with the ability to explain complex technical concepts to non-technical stakeholders.

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