Senior AIML Engineer

We are looking for a Senior AIML Engineer with good hands on experience in ML, DL and Gen AI with deployement.

• Minimum 5 years of experience in machine learning engineering and AI development.
• Deep expertise in machine learning algorithms and techniques like supervised/unsupervised learning, deep learning, reinforcement learning, etc.
• Solid experience in natural language processing (NLP) - language models, text generation, sentiment analysis, etc.
• Proven understanding of generative AI concepts like text/image/audio synthesis, diffusion models, transformers, etc.
• Expertise in Agentic AI and building real world applications using the same.
• Experience working with Agentic AI frameworks like Langgraph, ADK, Autogen etc.
• Hands-on experience developing and deploying generative AI applications (text generation, conversational AI, image synthesis, etc.)
• Experience in MLOps and ML model deployment pipelines.
• Proficiency in programming languages like Python, and ML frameworks like TensorFlow, PyTorch, etc.
• Knowledge of cloud platforms (AWS, GCP, Azure) and tools for scalable ML solution deployment.
• Experience with data processing, feature engineering, and model training on large datasets.
• Familiarity with responsible AI practices, AI ethics, model governance and risk mitigation.
• Understanding of software engineering best practices and applying them to ML systems.
• Experience with an agile development environment.
• Exposure in tools/framework to monitor and analyse ML model performance and data accuracy.
• Strong problem-solving, analytical, and communication abilities.
• Bachelor's or master's degree in computer science, AI, Statistics, Math or related fields.
  
  

Responsibilities

• Design, develop and optimize machine learning models for applications across different domains.
• Build natural language processing pipelines for tasks like text generation, summarization, translation, etc.
• Develop and deploy cutting-edge generative AI & Agentic AI applications.
• Implement MLOps practices - model training, evaluation, deployment, monitoring, and maintenance.
• Integrate machine learning capabilities into existing products or build new AI-powered applications.
• Perform data mining, cleaning, preparation, and augmentation for training robust ML models.
• Collaborate with cross-functional teams to translate AI requirements into technical implementations.
• Ensure model performance, scalability, reliability.
• Continuously research and implement state-of-the-art AI/ML algorithms and techniques.
• Manage the end-to-end ML lifecycle from data processing to production deployment.