Vice President of Machine Learning

Role Overview

Practice Head for Machine Learning Systems

built and scaled ML engineering practices

Key Responsibilities

Practice Leadership

• Define the vision and roadmap for Cybage’s Machine Learning Systems practice, aligned with industry trends and client priorities.
• Build offerings and frameworks across

  ML model development, deployment, MLOps, generative AI, and responsible AI governance

  .
• Develop accelerators, reference architectures, and reusable assets to differentiate Cybage in the market.

Client Consulting & Business Growth

• Lead

  consultative workshops

  with client executives to co-create ML/AI strategies, adoption roadmaps, and use-case portfolios.
• Partner with sales and account teams to drive presales solutioning, proposal creation, and thought leadership.
• Position Cybage as a

  strategic partner

  for ML-driven transformations that are measurable and outcome-driven.

Delivery Excellence

• Oversee delivery of ML programs spanning

  PoCs, pilots, and scaled deployments

  across industries.
• Ensure robust

  MLOps and governance practices

  for model lifecycle management, monitoring, retraining, and compliance.
• Provide architectural and technical guidance on ML stacks (e.g., TensorFlow, PyTorch, Hugging Face, MLflow, AWS Sagemaker, Azure ML, GCP Vertex AI, Databricks ML).
• Drive

  service-based and outcome-based engagement models

  , ensuring predictability and value delivery.

Team & Capability Building

• Build and mentor a high-performing team of ML engineers, data scientists, and solution architects.
• Develop future leaders with consulting and solutioning depth, not just technical skill.
• Foster collaboration across adjacent practices (Big Data, Cloud, Platform Engineering) to deliver end-to-end AI solutions.

Qualifications

Experience

• 15+ years in IT services or consulting, with

  7+ years in ML/AI leadership or architecture roles

  .
• Proven ability to

  establish or grow an ML/AI practice

  , including team building, offering development, and client engagement.
• Experience with

  end-to-end ML lifecycle

  : data prep, feature engineering, model training, evaluation, deployment, monitoring.
• Exposure to

  service delivery models

  (consulting, managed services, outcome-based).
• Strong background in

  applied ML use cases

  (forecasting, personalization, anomaly detection, NLP, computer vision, GenAI).

Skills & Competencies

• Technical bent

  : ability to deep-dive into ML architectures, pipelines, and MLOps practices.

• Strategic mindset

  : connect ML initiatives to tangible business outcomes.

• Leadership

  : experience in building practices and leading distributed teams (does not need to be at massive scale).

• Client-facing presence

  : ability to run workshops, advise senior stakeholders, and simplify complex ML topics.
• Knowledge of

  AI governance, ethics, and compliance

  (responsible AI, data privacy, bias mitigation).