Senior Engineer AI

3 days ago

Cochin, Kerala, India Litmus7 Full time US$ 1,50,000 - US$ 2,00,000 per year
  • 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 in 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.

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