About the role
We are seeking a highly motivated Data Scientist/ML Engineer with 6-8 years of experience in designing, developing, deploying, and managing AI/ML solutions at scale. The ideal candidate should possess strong expertise in the complete machine learning lifecycle, from data preparation and model development to production deployment, monitoring, and optimization.
Responsibilities:
- Develop and deploy machine learning, deep learning, time-series forecasting, and generative AI solutions for business-critical use cases.
- Lead end-to-end AI model development, including data acquisition, feature engineering, model training, validation, deployment, and monitoring.
- Build scalable ML pipelines and production-grade AI applications using MLOps best practices.
- Collaborate with product managers, business teams, data engineers, and software developers to deliver impactful AI solutions.
- Implement model governance, versioning, performance monitoring, and retraining frameworks.
- Integrate AI models into enterprise applications through APIs and cloud-native architectures.
- Drive continuous improvement in model accuracy, reliability, explainability, and operational performance.
Requirements:
- Strong expertise in Python, SQL, machine learning, deep learning, time-series forecasting, NLP, LLMs, RAG, and computer vision.
- Hands-on experience with Scikit-Learn, TensorFlow, PyTorch, Pandas, and NumPy.
- Experience with MLOps tools such as MLflow, Azure ML, Model Garden, Vertex AI (Gemini Enterprise), SageMaker, Kubeflow, Docker, Kubernetes, and CI/CD pipelines.
- Knowledge and experience of Azure/AWS/GCP cloud platforms.
- Experience deploying and managing ML models in production environments at enterprise scale.