Role Overview
Cybage is seeking a Practice Head for Machine Learning Systems to lead our AI / ML capability within the CDAI business unit. This is a strategic leadership role that blends deep technical expertise in applied ML systems with practice-building, client consulting, and outcome-based delivery experience.
The role requires someone who has built and scaled ML engineering practices in IT services or consulting environments, is able to guide solutioning at a technical level, and can also engage clients in executive workshops to define AI adoption roadmaps.
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).