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.

Work arrangement
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