We are looking for a highly skilled and experienced Senior AI/ML Engineer to join our team. In this role, you will be responsible for designing, developing, and deploying advanced artificial intelligence (AI) and machine learning (ML) models and systems.

Key Responsibilities:

• Design and implement AI/ML models, including supervised, unsupervised, and reinforcement learning algorithms, to solve complex business problems.
• Develop and deploy end-to-end ML pipelines, including data preprocessing, model training, evaluation, and production deployment.
• Experiment with cutting-edge techniques and approaches to improve model accuracy, efficiency, and robustness.
• Continuously monitor and optimize models for performance and scalability in production environments.
• Architect and build scalable AI solutions that can be integrated into existing systems and products.
• Collaborate with software engineers and product managers to ensure AI/ML models are seamlessly integrated into the product lifecycle.
• Develop APIs and services to support AI-driven features and functionalities.
• Stay up to date with the latest research and developments in AI and ML, including emerging trends and technologies.
• Conduct research experiments to test new algorithms, frameworks, and methodologies.
• Contribute to the AI/ML community by publishing research papers, attending conferences, and participating in workshops.
• Perform data analysis to extract meaningful insights and identify patterns from large, complex datasets.
• Design and implement feature engineering strategies to enhance model performance.
• Provide technical mentorship and guidance to junior engineers and data scientists.

Technical Skills:

• Strong proficiency in programming languages such as Python, R, or Java.
• Good experience with Gen AI , LLMs and RAG based applications.
• Expertise in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Keras, Scikit-Learn).
• In-depth knowledge of deep learning techniques (CNNs, RNNs, Transformers) and NLP models (BERT, GPT, etc.).
• Experience with big data technologies (e.g., Apache Spark, Hadoop) and cloud platforms (AWS, Azure, GCP).
• Familiarity with MLOps tools and practices, including model versioning, monitoring, and deployment (e.g., MLflow, Kubeflow).
• Strong understanding of data structures, algorithms, and software design principles.