ML Chartered/Lead Engineer

Job Description 

At our company, we hold an uncompromising stance against mediocrity, committing ourselves to work that delivers audacious impact and is fueled by a pioneering spirit of continuous progress and innovation. This ambition is supported by leadership grounded in courage and clarity—ensuring alignment, accountability, and purpose in everything we do. Integrity shapes how we hire, recognize, and develop talent, with a focus on potential, ambition, and long-term growth. Together, we operate with collective strength, prioritizing meaningful results over unnecessary rituals and navigating challenges with unity and resolve. Above all, we remain steadfast and resilient, never losing sight of our commitments and continuously pushing forward through complexity with determination and purpose to create a better life for all. 

• Architect, deploy and scale production-grade machine learning systems across customer experience, network operations and business decision domains.

• Own the end-to-end ML lifecycle – problem framing, data pipelines, feature engineering, model training, validation, deployment, monitoring and retraining.

• Establish enterprise-grade MLOps frameworks – CI/CD pipelines, automated retraining, monitoring, experiment tracking, feature stores and model registries.

• Lead development of forecasting, recommendation, personalization, churn prediction, geospatial, network optimization, anomaly detection and real-time decision systems.

• Build reusable ML services, APIs and feature pipelines, and optimize inference systems for scalability, latency and reliability.

• Mentor ML engineers and data scientists; define AI engineering best practices, coding standards, and solution governance across the team.

• Partner with data science, software engineering, network and business teams to translate AI prototypes and business requirements into scalable enterprise solutions.

Academic Qualification

• Bachelor’s degree in Computer Science, AI, Data Science, Statistics, Software Engineering, or related field from a reputed university

• A Master’s degree in a related field will be an added advantage

Job Experience Required

• Minimum 4-7+ years of experience in machine learning engineering, AI systems deployment, data engineering, backend engineering or production-scale analytics systems

• Proven experience deploying ML models into enterprise production environments; telecom or large-scale digital platform experience is preferred

Required Capabilities

• Strong expertise in Python and software engineering principles; deep understanding of supervised/unsupervised learning, ensemble methods, deep learning, feature engineering, time-series forecasting and statistical modelling

• Hands-on experience with Scikit-learn, PyTorch or TensorFlow, Apache Spark, MLflow, Docker, Kubernetes and Apache Airflow

• Hands-on familiarity with cloud-native ML on AWS, Google Cloud or Microsoft Azure, including distributed computing, real-time inference, microservices and batch/streaming pipelines

• Proven ability to design CI/CD for ML, feature stores, model registries, monitoring, explainability and governance frameworks

• Exposure to GenAI/LLM applications, geospatial analytics, graph analytics, recommendation systems and AI-driven optimization is a plus

• Strong leadership, mentoring, stakeholder management and ability to balance hands-on engineering with strategic technical direction