Join athenahealth as a Machine Learning Engineer (Associate level) and help build production AI/ML solutions that improve healthcare outcomes. This in-person role is based in Pune, India, and focuses on developing, evaluating, and deploying machine learning models across clinical and operational products. The role requires collaboration with product, engineering, and data teams to move models from prototype to production. This position reports to the Senior Engineering Manager. You will be part of the Data Science team within the Athena Clinicals Division, where you will develop and deploy machine learning models across a variety of healthcare products and domains. The team focuses on bringing machine learning to bear against the hardest problems in healthcare, partnering with product managers, clinicians, and engineering teams to translate clinical and operational problems into measurable ML use cases. Your work will involve the full model lifecycle, from exploratory analysis to monitored production deployments, using methods ranging from traditional supervised learning to modern deep learning and generative AI. **Key Responsibilities:**
Develop production-ready machine learning and deep learning models using Python and relevant libraries. - Implement and evaluate complex neural network architectures (NLP and/or computer vision) for healthcare use cases. - Design and build data pipelines and feature engineering workflows. - Integrate models into scalable production environments using containerization and orchestration patterns. - Optimize model performance, conduct error analysis, and design rigorous validation and monitoring processes. - Collaborate with product managers, clinicians, and engineers to translate clinical problems into measurable ML solutions and acceptance criteria. - Evaluate and adopt deep learning frameworks, transformer-based models, and foundational model techniques (LLMs/GenAI) to solve product problems. - Apply prompt engineering and optimization practices to improve generative AI outputs and alignment with product requirements. - Integrate AI and generative-model capabilities into development workflows, evaluate new AI tools and model variants for product fit, prototype responsible uses, and recommend best practices for safe, reliable deployment of AI features that enhance clinical and operational decision-making. - Communicate technical trade-offs, design decisions, and model limitations clearly in writing and presentations. **Qualifications Required:**
Bachelors or Masters degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related field (or equivalent practical experience). - 23 years of hands-on experience building and deploying machine learning or deep learning models in production. - Proficiency in Python, SQL, and Unix/Linux environments. - Experience developing and implementing deep learning models with complex neural network architectures. - Familiarity with deep learning frameworks (such as PyTorch or TensorFlow), transformer models, and libraries for NLP/vision. - Experience with LLMs, generative AI techniques, and prompt engineering; training and fine-tuning LLMs. - Familiarity with NLP or computer vision techniques and evaluation metrics. - Experience with cloud environments and infrastructure is beneficial; familiarity with AWS, Kubernetes, Kubeflow, or EKS is a plus. - Strong verbal and written communication skills for cross-functional collaboration and stakeholder-facing documentation. Join athenahealth as a Machine Learning Engineer (Associate level) and help build production AI/ML solutions that improve healthcare outcomes. This in-person role is based in Pune, India, and focuses on developing, evaluating, and deploying machine learning models across clinical and operational products. The role requires collaboration with product, engineering, and data teams to move models from prototype to production. This position reports to the Senior Engineering Manager. You will be part of the Data Science team within the Athena Clinicals Division, where you will develop and deploy machine learning models across a variety of healthcare products and domains. The team focuses on bringing machine learning to bear against the hardest problems in healthcare, partnering with product managers, clinicians, and engineering teams to translate clinical and operational problems into measurable ML use cases. Your work will involve the full model lifecycle, from exploratory analysis to monitored production deployments, using methods ranging from traditional supervised learning to modern deep learning and generative AI. **Key Responsibilities:**
Develop production-ready machine learning and deep learning models using Python and relevant libraries. - Implement and evaluate complex neural network architectures (NLP and/or computer vision) for healthcare use cases. - Design and build data pipelines and feature engineering workflows. - Integrate models
