Senior AI/ML Engineer

We are seeking highly skilled AI/ML Engineers to join our team. AI/ML Engineers code and develop software that deploys ML models and algorithms into production. Communicate and present complex analytics results and concepts to leadership and internal stakeholders. Employ AI and/or ML that may include natural language processing (NLP), natural language understanding (NLU), semantic understanding, intent classification, computer vision, deep learning, and automatic speech recognition (ASR). Apply deep learning technologies to give computers the capability to visualize, learn and respond to complex situations. May adapt machine learning to areas such as artificial intelligence, robotics and other products that allow users to have an interactive experience. Work with large scale computing frameworks and data analysis systems. Employees in this role are expected to apply knowledge of experimental methodologies, statistics, optimization, probability theory and machine learning using code for tool building, statistical analysis, using both general purpose software and statistical languages

Key Responsibilities:

Design, develop, and implement machine learning models and algorithms to solve complex healthcare problems.

Design and implement AI/ML solutions using Python, along with libraries like pandas, NumPy, and SQL for data handling

Build and deploy models using frameworks such as Scikit-learn, TensorFlow, and PyTorch

Perform exploratory data analysis (EDA), statistical analysis, and feature engineering to extract meaningful insights from data

Develop, tune, and evaluate machine learning models for both predictive and prescriptive analytics

Conduct drift analysis to monitor model performance and maintain accuracy over time

Collaborate with cross-functional teams to translate business requirements into scalable technical solutions

Manage the full lifecycle of AI/ML projects, including data preparation, model development, optimization, and deployment

Ensure scalability, reliability, and efficiency of AI/ML systems in production environments

Stay up-to-date with the latest advancements in AI/ML tools and techniques, identifying opportunities to enhance existing solutions

Document methodologies, findings, and insights clearly for both technical and non-technical stakeholders

Participate actively in the full AI lifecycle: contributing to brainstorming and conceptualization, rapid prototyping and experimentation, and writing high-quality, robust production code

Utilize and contribute to reusable AI/ML platforms, components, and frameworks, helping to improve engineering efficiency

Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Key Skills:

Strong foundation in machine learning, deep learning, and model evaluation techniques

Proficient in Python, with hands-on experience using PyTorch, TensorFlow, and scikit-learn

Skilled in data preprocessing, feature engineering, and SQL-based data handling

Familiar with deploying APIs using Docker, FastAPI, and managing version control with Git

Experience working with cloud platforms such as AWS, GCP, and Azure, including scalable ML model deployment

Understanding of MLOps tools like MLflow and Kubeflow, and implementing CI/CD workflows for ML systems. Strong experience with Docker and Kubernetes (AKS) for cloud-native development.

Experience with message queues like Azure Service Bus.

In-depth knowledge of LLM vulnerabilities and implementation of guardrails.

Hands-on experience with FastAPI and Pydantic for building microservices.

Proficiency with explainable AI tools like LIME and SHAP.

Qualifications:

Bachelor's or Master's degree in Computer Science, AI/ML, or a related field.

10+ years of experience in AIML engineering and research

Core Experience: 5+ years of professional software engineering experience with a significant focus on building and deploying production-grade AI/ML models and systems.

Excellent problem-solving skills and the ability to work independently and collaboratively.

Strong communication skills and the ability to present complex technical concepts to non-technical stakeholders.

Familiarity with AIML governance, ethics, and responsible AI practices