AI/ML Technical Lead

Infosys; Bengaluru; On-site; INR 2,000,000 - 3,500,000; Full time

Job summary

Infosys seeks an experienced AI/ML leader to drive end-to-end ML initiatives, turning business goals into scalable model strategies. You will guide model selection, training, evaluation, and deployment for production-grade systems, while mentoring engineers and ensuring governance.

The role emphasizes NLP capabilities, experimentation practices, and collaboration with platform teams to deliver reliable, scalable ML solutions that impact business outcomes.

Qualifications

• 5–9 years of AI/ML solution delivery in real-world environments.
• Strong expertise in model development, training, evaluation and iterative improvement.
• Solid NLP experience with learning from data, 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).

Responsibilities

• Lead end-to-end AI/ML initiatives, translating business goals into model strategies and metrics.
• Provide technical direction on model selection, training, evaluation frameworks, and production deployment patterns.
• Mentor ML engineers and data scientists through design reviews and performance deep-dives.
• Drive standards for reproducibility, experimentation tracking, documentation, and governance.

Skills

MLOps

Model Monitoring

Drift Detection

NLP & Transformers

A/B Testing & Experimentation

Education

BTECH, MTECH, MCA, MSC

Job description

AI/ML Good to have skills

MLOps, Model Monitoring & Drift Detection, Feature Store, A/B Testing & Experimentation, Data Engineering Pipelines

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.

Key Responsibilities

• 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).