Litmus7 - Artificial Intelligence Engineer - Machine Learning Model (3-10 yrs)

Job Description :

- Minimum 3+ 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.

- 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

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 models for applications like conversational AI, text/image synthesis, etc.

- 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