Senior AI/ML Engineer

Job requirements

Experience Range: with at least 6 years of experience in AI/ML engineering, including hands-on expertise in designing, building, and operationalizing enterprise-scale AI platforms Key Responsibilities:

Lead the design and architecture of enterprise-scale AI platforms, ensuring scalability, security, and operational readiness

Define and implement frameworks for model lifecycle management, MLOps/LLMOps, and AI observability to support robust deployment and monitoring

Establish and enforce Responsible AI principles, governance frameworks, and technical guardrails across AI solutions

Drive platform-level technical decision-making and define reusable architecture patterns, standards, and reference models for AI and GenAI solutions

Collaborate with business, data, technology, and platform teams to translate AI architecture principles into production-ready capabilities

Evaluate emerging AI technologies and assess their applicability to enterprise AI platforms, supporting long-term scalability and business outcomes

Maintain and improve AI model governance, version control, and documentation for ongoing operational excellence

Required Skills:

Expertise in enterprise-scale AI platform design and operationalization

Deep proficiency in model lifecycle management, MLOps/LLMOps, and AI observability

Advanced programming skills in Python and PySpark

Experience with cloud-based AI platforms and modern data/AI architectures

Knowledge of Responsible AI, AI governance, model risk, security, and compliance

Hands-on experience with KubeFlow and BentoML for ML pipeline orchestration

Competence in classification algorithms such as decision trees and SVM

Experience with Great Expectations and Evidently AI for model validation

Strong proficiency in regression analysis (linear and logistic)

Statistical analysis and computing for large datasets

Preferred Skills:

Experience with GenAI architectures, large language models, and AI agents

Proficiency in advanced ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, or MXNet

Knowledge of vector databases and AI orchestration

Understanding of AI observability and Responsible AI frameworks

Experience developing reusable AI architecture patterns and accelerators

Desired Qualifications:

Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline

Certification in Machine Learning or Data Science (e.g., TensorFlow Developer Certificate, Microsoft Certified: Azure AI Engineer Associate)

Relevant certification in statistical analysis or advanced analytics (e.g., SAS Certified Specialist, IBM Data Science Professional Certificate)