- Company Name
- IPrime Info Solutions Inc.
- Job Title
- Senior Ai/ML Engineer
- Job Description
-
Job title: Senior AI/ML Engineer
Role Summary
Lead the design, development, deployment, and scaling of enterprise‑level AI/ML solutions across multiple domains. Own the full machine‑learning lifecycle, from data acquisition to model governance, while mentoring a team of data scientists and engineers.
Expectations
- Demonstrate 15+ years of IT experience with 8+ years in AI/ML.
- Deliver production‑grade, scalable solutions that meet performance, reliability, security, and compliance standards.
- Drive AI strategy, best practices, and responsible AI adoption at the organizational level.
Key Responsibilities
- Architect, develop, and deploy large‑scale AI/ML platforms (Deep Learning, NLP, CV, Reinforcement Learning, Predictive Analytics).
- Lead end‑to‑end ML lifecycle: data acquisition, preprocessing, feature engineering, modeling, validation, deployment, monitoring, and optimization.
- Build, train, and fine‑tune models using TensorFlow, PyTorch, Keras, Scikit‑learn, and other DL/ML frameworks.
- Design and implement MLOps pipelines (MLflow, Kubeflow, SageMaker, CI/CD) on AWS, Azure, or GCP.
- Integrate ML models into production as RESTful APIs; ensure scalability and robustness.
- Collaborate with data engineers, product managers, architects, and stakeholders to align AI initiatives with business goals.
- Mentor junior ML engineers and data scientists; enforce coding standards and Model Governance.
- Manage large structured and unstructured datasets using Spark, Hadoop, and distributed computing.
- Stay current on emerging AI/ML research, tools, and industry trends; advise on technology adoption.
Required Skills
- Programming: Python, R, Scala.
- ML/DL frameworks: TensorFlow, PyTorch, Keras, Scikit‑learn.
- Strong statistical, probabilistic, linear algebra, and optimization knowledge.
- Big Data platforms: Apache Spark, Hadoop.
- Cloud proficiency: AWS, Azure, or GCP.
- MLOps tools: MLflow, Kubeflow, SageMaker, CI/CD pipelines for ML.
- Databases: SQL and NoSQL.
- Containerization & orchestration: Docker, Kubernetes.
- RESTful API development for ML services.
Required Education & Certifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or related field (advanced degree preferred).
- Professional certifications such as AWS Certified Machine Learning – Specialty, GCP Cloud Machine Learning Engineer, or Azure AI Engineer are desirable.