ML Engineer

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.