Senior Engineer - AI ML

PRINCIPLE RESPONSIBILITIES: AI/ML Platform Development Implement and maintain components of the centralized enterprise AI/ML platform, including model serving, inference APIs, and shared ML libraries. Build and operate robust, production-grade ML pipelines covering data ingestion, feature engineering, model training, deployment, and inference. Contribute to the development of shared feature stores, model registries, and inference services used by multiple product teams. MLOps & Model Lifecycle Develop and maintain CI/CD pipelines for ML model training, evaluation, and deployment. Implement automated model monitoring, drift detection, and alerting mechanisms. Build tooling to support model versioning, lineage tracking, and rollback capabilities. Performance & Reliability Engineering Optimize inference pipelines for latency, throughput, and resource efficiency. Contribute to scalability improvements to handle growing model counts and inference request volumes. Investigate and resolve production issues in the AI/ML platform. Reusability & Inner-Sourcing Develop well-documented, reusable ML components and libraries for consumption by product engineering teams. Actively participate in and contribute to the inner-source AI/ML asset ecosystem. Write technical documentation, runbooks, and integration guides for shared services. Collaboration & Engineering Excellence Work closely with data scientists, ML engineers, and product architects to translate model requirements into production implementations. Participate in architectural and code reviews, upholding high standards for quality, security, and maintainability. Mentor junior engineers on AI/ML engineering best practices. REQUIRED KNOWLEDGE, SKILLS AND EXPERIENCE: Experience and Expertise 5+ years of software engineering experience, with at least 3 years focused on building and operating AI/ML systems in production. Demonstrated experience building and maintaining ML pipelines and model serving infrastructure at scale. Hands-on experience with MLOps practices including CI/CD for ML, model monitoring, and lifecycle management. Proven ability to build reliable, maintainable software components used by multiple teams. Technical Skills Strong proficiency in Python; familiarity with Go or Java is a plus. Hands-on experience with major AI/ML frameworks: TensorFlow and/or PyTorch. Practical knowledge of MLOps tools: MLflow, Kubeflow, or equivalent (Vertex AI, SageMaker, Azure ML). Solid experience with containerization (Docker) and orchestration (Kubernetes) for ML workloads. Experience with data processing technologies (Spark, Kafka) and data storage (data lakes, feature stores, data warehouses). Familiarity with at least one public cloud AI/ML ecosystem (AWS, GCP, or Azure). Experience designing and consuming RESTful or gRPC APIs for inference services. Analytical and Problem-Solving Skills Strong debugging and root-cause analysis skills for distributed AI/ML systems. Ability to profile and optimize ML inference for latency and cost. Collaboration & Communication Clear written and verbal communication; ability to document technical systems for a broad audience. Collaborative working style with data scientists, engineers, and architects. Educational Background Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field. Additional Skills Experience in the telecom domain or with OSS (Operations Support Systems) data and use cases. Familiarity with GPU/TPU-based inference optimization. Knowledge of responsible AI practices (fairness, explainability, privacy). Contributions to open-source MLOps or AI infrastructure projects. Experience wi PRINCIPLE RESPONSIBILITIES: AI/ML Platform Development Implement and maintain components of the centralized enterprise AI/ML platform, including model serving, inference APIs, and shared ML libraries. Build and operate robust, production-grade ML pipelines covering data ingestion, feature engineering, model training, deployment, and inference. Contribute to the development of shared feature stores, model registries, and inference services used by multiple product teams. MLOps & Model Lifecycle Develop and maintain CI/CD pipelines for ML model training, evaluation, and deployment. Implement automated model monitoring, drift detection, and alerting mechanisms. Build tooling to support model versioning, lineage tracking, and rollback capabilities. Performance & Reliability Engineering Optimize inference pipelines for latency, throughput, and resource efficiency. Contribute to scalability improvements to handle growing model counts and inference request volumes. Investigate and resolve production issues in the AI/ML platform. Reusability & Inner-Sourcing Develop well-documented