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.