AI/ML

AI/ML

• Primary skills:Technology->AI-AI Engineering->AI/ML Solution Architecture and Design Minimum Qualifications: • Bachelor’s degree (or equivalent) in Engineering/Technology/Computer Science or related field (e.g., BE/BTech/BSc). • 5–9 years of overall experience with strong, hands-on expertise in AI/ML solution development and delivery. • Proven experience applying machine learning techniques to real-world datasets, including model evaluation and iteration. • Practical experience with NLP and data learning workflows, including preparing and analyzing text and structured data. • Ability to lead technical discussions, translate requirements into approaches, and guide teams toward outcomes. Preferred Qualifications: • Master’s degree (or equivalent) such as MTech/MCA/MSc in a relevant discipline. • Experience leading consulting-style engagements, including stakeholder management, estimation, and delivery governance. • Strong track record of deploying AI/ML solutions into production environments with monitoring and iteration practices. • Experience designing NLP pipelines for domain-specific problems and improving performance through experimentation. • Ability to define reusable frameworks, accelerators, and standards that improve team productivity and quality. Good to have skills: MLOps, Model Monitoring, Feature Engineering, Data Visualization, Cloud Platforms Key Responsibilities: • Lead end-to-end AI/ML engagements: discovery, solution design, development, validation, and rollout. • Partner with business and technical stakeholders to define use cases, success metrics, and implementation roadmaps. • Design and build machine learning models aligned to business objectives, ensuring robust evaluation and performance tracking. • Drive NLP solution development (e.g., text classification, entity extraction, search/relevance) based on problem needs. • Establish best practices for data preparation, feature engineering, model selection, and experimentation workflows. • Review model results and communicate insights, trade-offs, and recommendations to both technical and non-technical audiences. • Collaborate with engineering teams to support model operationalization, monitoring, and continuous improvement. • Mentor junior team members, conduct technical reviews, and contribute to reusable assets and accelerators.