In this role, you will be responsible for designing, developing, and deploying AI models and end-to-end intelligent systems that address complex business challenges. You will work closely with global teams, leveraging MLOps practices to streamline deployment workflows, ensure model accuracy, and maintain operational excellence.

You will also collaborate with stakeholders to bridge the gap between technical AI capabilities and business impact, delivering solutions that are both innovative and practical.

Key Responsibilities :

- AI/ML Model Development : Build, train, and fine-tune machine learning and deep learning models using Python for diverse use cases.

- End-to-End AI Lifecycle : Manage the complete AI development lifecycle from data acquisition, preprocessing, and feature engineering to deployment and monitoring.

- MLOps Implementation : Design and implement CI/CD pipelines for ML models using tools such as MLflow, Kubeflow, AWS SageMaker, or GCP Vertex AI.

- Cloud-Based AI Solutions : Architect and deploy AI workloads on cloud AI platforms (AWS, GCP, Azure) ensuring performance, scalability, and security.

- Containerized Deployments : Leverage Docker, Kubernetes, and OpenShift to package and scale AI solutions efficiently.

- Natural Language Processing (NLP) : Apply NLP techniques for text classification, sentiment analysis, chatbots, and other language-based applications using libraries such as spaCy, NLTK, Hugging Face Transformers, or OpenAI APIs.

- Performance Optimization : Continuously improve AI models and systems to enhance speed, accuracy, and cost-effectiveness.

- Stakeholder Communication : Present complex AI concepts to non-technical audiences in a clear, concise, and impactful manner.

- Best Practices : Follow industry standards for code quality, reproducibility, and security compliance in AI development.

Preferred Qualifications :

- Strong proficiency in Python and popular AI/ML libraries (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).

- Demonstrated expertise in cloud-based AI platforms such as AWS AI/ML, GCP AI, or Azure Cognitive Services.

- Deep understanding of MLOps frameworks and best practices for deployment, scaling, and monitoring.

- Hands-on experience with container orchestration (Docker, Kubernetes, OpenShift).

- Proven track record in NLP model design and integration into real-world applications.

- Solid understanding of distributed computing for AI workloads.

- Exceptional problem-solving skills with a focus on delivering business impact.

- Strong verbal and written communication skills for effective cross-functional collaboration.