Overview

Primary Responsibilities: • Machine Learning Development • Design, develop, train, evaluate, and deploy machine learning models supporting: • Predictive analytics • Forecasting • Recommendation systems • Classification and regression • Anomaly detection • Translate business requirements into scalable AI/ML solutions • Apply machine learning, statistical modeling, and data scien

Responsibilities

• Machine Learning Development
• Design, develop, train, evaluate, and deploy machine learning models supporting:
• Predictive analytics
• Forecasting
• Recommendation systems
• Classification and regression
• Anomaly detection
• Translate business requirements into scalable AI/ML solutions
• Apply machine learning, statistical modeling, and data science techniques to solve business problems
• Perform exploratory data analysis (EDA), feature engineering, data preparation, and model experimentation
• Work with structured, semi-structured, and unstructured datasets
• AI/ML Engineering & Model Lifecycle
• Build and maintain machine learning pipelines supporting:
• Data ingestion
• Feature engineering
• Model training
• Model validation
• Model deployment
• Monitoring and retraining
• Implement model evaluation, benchmarking, and performance measurement processes
• Support model optimization and hyperparameter tuning activities
• Contribute to repeatable and scalable AI engineering practices
• MLOps & Production Deployment
• Deploy machine learning models using APIs, containerized services, and cloud-native platforms
• Support MLOps practices including:
• Experiment tracking
• Model versioning
• Deployment automation
• CI/CD integration
• Model lifecycle management

Qualifications

• Bachelor's degree in computer science, Data Science, Engineering, Mathematics, Statistics, Artificial Intelligence, or related field
• 6+ years of experience in Machine Learning, Artificial Intelligence, Data Science, Software Engineering, or related disciplines
• Experience developing and deploying machine learning solutions in enterprise or cloud environments
• Experience building machine learning pipelines and production-ready AI solutions
• Experience working with APIs, cloud-based AI services, and distributed data platforms
• Experience integrating AI/ML solutions into business applications and workflows
• Knowledge of model monitoring, performance evaluation, and production support processes
• Understanding of Responsible AI, model governance, and compliance requirements
• Solid understanding of:
• Machine Learning
• Statistical Modeling
• Predictive Analytics
• Model Evaluation
• Feature Engineering
• Familiarity with MLOps practices including model deployment, monitoring, experiment tracking, and lifecycle management
• Solid programming skills in Python and SQL
• Demonstrated solid analytical, problem-solving, communication, and collaboration skills

Nice to have

• Experience deploying machine learning solutions using Azure ML, SageMaker, Vertex AI, MLflow, Kubeflow, or similar platforms
• Experience with distributed data processing technologies including Spark, Databricks, PySpark, Kafka, or modern data engineering platforms
• Experience developing machine learning and deep learning solutions using TensorFlow, PyTorch, or equivalent frameworks
• Experience with Generative AI technologies including:
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Embeddings
• Semantic Search
• Agentic AI concepts
• Experience integrating AI services and model APIs into enterprise applications
• Experience contributing to reusable AI frameworks, engineering accelerators, or platform capabilities
• Experience working within healthcare, financial services, insurance, or other regulated industries
• Familiarity with NLP, recommendation systems, forecasting, anomaly detection, or intelligent automation solutions
• Familiarity with model monitoring, observability, and operational analytics practices
• Understanding of Responsible AI, model risk management, and governance frameworks
• Contributions to AI innovation initiatives, open-source projects, technical publications, or enterprise transformation efforts