Senior AIML Engineer

About the Role

We seek an experienced Senior AIML Engineer proficient in machine learning, deep learning, and generative AI, with a strong focus on deployment. The ideal candidate will have at least five years of experience in ML engineering and AI development, showcasing expertise in a wide range of machine learning algorithms including supervised, unsupervised, deep, and reinforcement learning. They should have substantial knowledge of natural language processing techniques, including language models, text generation, and sentiment analysis.

The role demands familiarity with generative AI concepts such as text, image, and audio synthesis, diffusion models, transformers, and practical experience building real-world applications using Agentic AI frameworks like Langgraph, ADK, and Autogen. A solid background in developing and deploying generative AI applications as well as MLOps and ML model deployment pipelines is essential.

Responsibilities

• Design and optimize machine learning models applicable to various domains.
• Develop NLP pipelines for tasks such as text generation, summarization, and translation.
• Create and deploy innovative generative AI and Agentic AI solutions.
• Implement MLOps protocols encompassing training, evaluation, deployment, monitoring, and upkeep of ML models.
• Integrate ML functionalities into existing products or develop new AI-driven applications.
• Process, clean, prepare, and augment data to build robust models.
• Work collaboratively across teams to convert AI requirements into actionable technical solutions.
• Ensure ML models meet performance, scalability, and reliability standards.
• Keep updated with and apply the latest advances in AI/ML algorithms and approaches.
• Manage the complete ML lifecycle from data intake to production deployment.

Requirements

• Over five years of professional experience in machine learning engineering and AI development.
• In-depth understanding of machine learning techniques like supervised, unsupervised, deep learning, and reinforcement learning.
• Proficient in natural language processing methods, including language generation and sentiment analysis.
• Strong grasp of generative AI technologies, including synthesis of text, images, and audio; diffusion models and transformer architectures.
• Experience with Agentic AI and practical know-how of frameworks such as Langgraph, ADK, and Autogen.
• Hands-on experience with deployment and development of generative AI applications.
• Knowledge of MLOps practices and pipelines.
• Programming expertise in Python and familiarity with ML frameworks like TensorFlow and PyTorch.
• Competence in cloud platforms such as AWS, Google Cloud, and Azure for scalable deployment.
• Skilled in data processing, feature engineering, and training models on large datasets.
• Awareness of AI ethics, responsible AI methodologies, governance, and risk management.
• Experience working in agile software development environments.
• Ability to monitor and analyze ML performance and data quality using specialized tools.
• Excellent problem-solving skills and effective communication.
• Bachelor's or Master's degree in computer science, AI, statistics, mathematics, or related fields.