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- About the job
- Step into a high impact leadership role where you ll shape intelligent products and guide teams in building practical scalable AI solutions
- You ll work at the intersection of machine learning NLP and data driven experimentation turning complex business problems into models that deliver measurable outcomes
- This role is ideal for someone who enjoys mentoring engineers setting technical direction and collaborating closely with product data and engineering stakeholders
- You ll champion best practices across the ML lifecycle from data preparation and feature engineering to model deployment and continuous improvement while fostering a culture of curiosity ownership and learning
- If you re excited to lead with both technical depth and a collaborative mindset this is a chance to build meaningful AI capabilities and help teams deliver smarter experiences at scale

Key Responsibilities:

- 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
- Solid 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

Technical Requirements:

- Good to have skills
- MLOps Model Monitoring Drift Detection Feature Store A B Testing Experimentation Data Engineering Pipelines

Additional Responsibilities:

- 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

Preferred Skills:

Technology->AI-AI Engineering->AI/ML Solution Architecture and Design

📌 AI/ML (Bengaluru)
🏢 Infosys
📍 Bengaluru