Ai Ml Engineer

Design, develop, and deploy scalable AI/ML models to solve complex business challenges.
•Lead end-to-end ML workflows, including data preprocessing, model training, hyperparameter tuning, and deployment.•Apply classical machine learning techniques (e.g., boosting, ensemble methods) and modern deep learning frameworks (e.g., PyTorch, TensorFlow) to deliver innovative solutions.•Work with large datasets using tools such as PySpark, Databricks, Pandas, and Numpy to enable data-driven decision-making.•Develop and fine-tune Generative AI and large language models (LLMs) using tools like Hugging Face, LangChain, and similar frameworks.•Collaborate with cross-functional teams to integrate AI/ML solutions with existing systems and applications.•Leverage cloud platforms like Azure AI, Azure ML, Google Cloud Platform, and OpenAI to build and deploy AI/ML models at scale.•Utilize ML lifecycle management tools (e.g., MLflow) to track and monitor model performance.•Conduct thorough data analysis using SQL to derive actionable insights and optimize model performance.•Explore and implement conversational AI systems (e.g., Dialogflow, Vertex AI) to enhance user interactions.•Stay up-to-date with the latest advancements in AI/ML technologies and recommend their adoption where relevant.SkillDesired Experience (Years)Overall hands-on AI/ML project development using Python programming5+Deep learning using PyTorch5+Classical machine learning (e.g., sklearn, boosting, NLP)5+Data analysis using SQL5+Working with large datasets using PySpark, Databricks, Pandas, Numpy, etc.2+Experience with Transformers, Hugging Face, LLMs, Generative AI, LangChain2+Proficiency with Azure AI, Azure ML, OpenAI2+ML model deployment (e.g., MLflow, Databricks, Azure)2+Healthcare domain and system knowledge1+Familiarity with Google Cloud Platform (e.g., Dialogflow, Vertex AI)1+'

Qualifications - External

Bacherlor's or Master's in Computer Science, Data Science, Machine Learning, or a related field.
Prior experience in the healthcare domain, including working with clinical or patient datasets.Hands-on experience with Generative AI applications and building conversational agents.Strong understanding of data engineering principles, including ETL pipelines and data workflows.Proven ability to handle unstructured datasets, such as text, images, and audio.Excellent problem-solving and critical-thinking skills.Strong communication skills and the ability to collaborate with cross-functional stakeholders.'