We are seeking an experienced Machine Learning Solutions Lead Specialist Engineer to architect and deliver advanced machine learning and AI-driven solutions across complex enterprise environments. The ideal candidate will have deep expertise in machine learning frameworks, scalable model deployment, cloud-based ML platforms, and experience leading teams to translate business challenges into actionable ML strategies. This role involves end-to-end project ownership including problem definition, data engineering collaboration, model development, MLOps automation, and implementation of production-ready ML systems.
-
Lead the design, development, and deployment of scalable machine learning models, algorithms, and advanced analytics solutions.
-
Work closely with business stakeholders to identify opportunities for ML-driven automation and intelligent insights.
-
Drive end-to-end ML lifecycle including data exploration, feature engineering, model training, validation, and production deployment.
-
Architect AI/ML systems using modern cloud platforms such as AWS, Azure, or Google Cloud.
-
Implement MLOps best practices for CI/CD pipelines, model versioning, monitoring, and automated retraining.
-
Develop reusable ML frameworks, tools, and libraries to support predictive analytics and real-time decision systems.
-
Collaborate with data engineers, data scientists, and software development teams to integrate models within enterprise platforms.
-
Conduct performance tuning, evaluation, and testing of ML models to ensure accuracy, reliability, scalability, and ethical compliance.
-
Mentor and provide technical leadership to junior engineers and data science team members.
-
Document solution architectures and present technical strategies to leadership and cross-functional teams.
-
12+ years of experience in machine learning, artificial intelligence, or advanced data science roles.
-
Strong expertise in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost.
-
Solid understanding of distributed computing technologies such as Spark, Ray, or Dask.
-
Hands-on experience with cloud-based ML platforms including AWS SageMaker, Azure ML, or GCP Vertex AI.
-
Proven experience building and deploying large-scale ML pipelines and production-grade AI solutions.
-
Strong background in statistics, probability, optimization techniques, and feature engineering methods.
-
Experience with MLOps tools such as MLflow, Kubeflow, Airflow, Docker, and Kubernetes.
-
Strong problem-solving, analytical, and communication skills.
-
Experience working with LLMs, generative AI, and transformer-based architectures.
-
Familiarity with real-time inference systems, streaming platforms, and event-driven processing such as Kafka or Flink.
-
Experience with data governance, model explainability, fairness, and compliance frameworks.
-
Knowledge of domain-specific ML applications such as forecasting, recommendation engines, NLP, computer vision, or reinforcement learning.
-
Previous experience in leading AI-driven transformation programs or consulting environments.
-
Advanced degree (Master's or Ph.D.) in Computer Science, Data Science, Machine Learning, Mathematics, or related field.