AI ML Engineer

Primary Responsibilities:

• Design, develop, and deploy machine learning models and algorithms into production applications and services
• Build end‑to‑end ML pipelines including data preparation, training, evaluation, deployment, and monitoring
• Apply ML techniques such as NLP/NLU, intent classification, semantic understanding, deep learning, computer vision, and/or speech recognition based on use case needs [
• Work with large scale computing frameworks and data analysis systems to support model training and inference at scale
• Use experimental methodologies and metrics to validate models; iterate based on performance, bias/variance, and generalization behavior
• Communicate and present complex analytics and model outcomes clearly to leadership and internal stakeholders

• Partner with cross‑functional teams (Product, Data, Engineering) to translate requirements into AI/ML solutions and deliver measurable outcomes

• 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

Required Qualifications:

• Undergraduate degree or equivalent experience
• 5+ years of hands‑on experience building and deploying ML solutions into production environments
• Experience working with large datasets and scalable compute/data processing frameworks
• Solid foundation in statistics, optimization, probability, and machine learning concepts used in production ML engineering
• Proven solid programming skills (Python preferred) and experience implementing ML algorithms and experimentation workflows
  • Proven ability to explain model behavior, tradeoffs, and outcomes to both technical and non‑technical stakeholders