AI/ML

Good to have skills: MLOps, Model Monitoring & Drift Detection, Feature Store, A/B Testing & Experimentation, Data Engineering Pipelines

Key Responsibilities: Technical Leadership & Delivery

• Lead end-to-end AI/ML initiatives, translating business goals into model strategies, milestones, and measurable success metrics.
• Provide technical direction on model selection, training approaches, evaluation frameworks, and deployment patterns for production-grade ML systems.
• Mentor and guide ML engineers and data scientists through design reviews, code reviews, and model performance deep-dives.
• Drive engineering excellence by defining standards for reproducibility, experimentation tracking, documentation, and model governance. Model Development (AI/ML, NLP, Data Learning)
• Build and optimize machine learning models using structured and unstructured data, ensuring robustness, generalization, and interpretability where needed.
• Design and implement NLP pipelines for tasks such as text classification, entity extraction, semantic search, summarization, or intent detection based on product needs.
• Partner with data stakeholders to improve data learning workflows: data quality checks, feature engineering, labeling strategies, and feedback loops.
• Establish model evaluation practices including offline metrics, error analysis, bias checks, and A/B testing where applicable. Collaboration & Stakeholder Management
• Collaborate with product and engineering teams to align model capabilities with user experience, latency, scalability, and reliability requirements.
• Communicate technical trade-offs and model outcomes clearly to both technical and non-technical stakeholders.
• Identify risks early (data drift, model decay, dependency gaps) and drive mitigation plans to ensure stable delivery. Minimum Qualifications:
• 5–9 years of overall experience with strong hands-on ownership of AI/ML solution delivery in real-world environments.
• Strong expertise in AI/ML including model development, training, evaluation, and iterative improvement.
• Solid experience in NLP and applied learning from data (data learning workflows, feature engineering, and experimentation).
• Ability to lead technical discussions, mentor team members, and drive execution across multiple workstreams.
• Education: BTECH, MTECH, MCA, MSC (or equivalent). Preferred Qualifications:
• Proven experience leading production ML deployments, including monitoring, retraining strategies, and performance optimization over time.
• Strong understanding of modern NLP approaches (transformer-based modeling, embeddings, prompt-based workflows) and how to evaluate them reliably.
• Experience designing scalable ML architectures and collaborating closely with platform/engineering teams to operationalize models.
• Demonstrated ability to define best practices for experimentation, versioning, and model governance across teams.
• Track record of delivering measurable business impact through ML initiatives and influencing stakeholders with data-backed recommendations.