Delivery Manager - AIML LTIMindtree Limited

About LTIMindtree Limited

Job Description

Skills : Delivery Manager - AIML

Location : Bangalore

Experience : 16 - 22 Years

Job Description :

End-to-End Project Delivery:
Lead and manage the full lifecycle of AI/ML projects from initiation through deployment and maintenance.

Team Leadership:
Oversee cross-functional teams including data scientists, ML engineers, MLOps specialists, business analysts, and developers.

Stakeholder Management:
Interface with internal and external stakeholders to define requirements, scope projects, and communicate progress and risks.

Project Governance & Methodology:
Apply delivery best practices, including Agile, DevOps, and AI-specific workflows (e.g., model lifecycle management, responsible AI practices).

Risk and Quality Management:
Identify risks early and implement mitigation strategies while maintaining quality standards in model performance, scalability, and compliance.

Technical Oversight:
Provide technical guidance on AI architectures, data pipelines, model deployment, and monitoring. Work closely with solution architects and technical leads.

Budget and Resource Planning:
Manage project budgets, forecast resource needs, and optimize team utilization.

Client Engagement & Growth:
Support business development by contributing to proposals, solutioning, and roadmap planning with AI capabilities.

Required Qualifications:
1015 years of experience in project/program management with at least 46 years focused on AI/ML or advanced analytics delivery.

Strong understanding of AI/ML technologies, platforms (e.g., AWS/GCP/Azure ML, TensorFlow, PyTorch, etc.), and AI governance practices.

Demonstrated ability to deliver AI solutions in production environments, preferably at scale in regulated or enterprise settings.

Proven experience managing multi-disciplinary teams across geographies.

Excellent communication, negotiation, and presentation skills.

PMP, PRINCE2, or Agile certifications are a plus.

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. MBA is a plus.

Preferred Experience:
Exposure to domains such as finance, healthcare, manufacturing, or retail with real-world AI deployment experience.

Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow, etc.).

Familiarity with ethical AI principles, data privacy regulations (e.g., GDPR), and model risk management."