Lead Data/ai Engineering

We are seeking an innovative and experienced Lead Data AI Engineering to join our team The ideal candidate will be a visionary in Generative AI Machine Learning and Data Engineering with a proven track record of designing developing and deploying scalable AI solutions This role will collaborate closely with cross-functional teams to drive data-driven strategies and deliver impactful business outcomes Overall Purpose The Lead Data Scientist will lead the development and implementation of advanced AI and machine learning models leveraging cutting-edge techniques in Generative AI and data engineering The position is responsible for providing technical leadership mentoring team members and ensuring successful delivery of complex data science projects that support our business objectives - it is an Individual Contributor IC role Key Roles and Responsibilities Lead the design development and deployment of advanced Generative AI and ML models for diverse business use cases Architect scalable data pipelines and data engineering solutions to support AI ML projects Collaborate with business stakeholders product managers and engineering teams to translate requirements into actionable data science solutions Architect and develop LLM applications prompt engineering RAG tool use agents evaluation safety guardrails Deliver classical ML and deep learning solutions for forecasting anomaly detection churn CLV propensity fraud spam NLP and recommendation systems Optimize models for performance latency and cost quantization distillation caching Mentor and guide junior data scientists and data engineers fostering a culture of technical excellence and continuous learning Ensure best practices in model development version control model monitoring and documentation Develop and enforce data governance security and compliance standards within data science initiatives Communicate complex technical concepts and results to both technical and non-technical stakeholders Drive the adoption of MLOps and automation tools to streamline the model lifecycle Evaluate and implement tools frameworks and infrastructure to enhance data science capabilities Stay current with the latest research and advancements in AI ML and assess their applicability to business challenges Required Skills Advanced degree Master s or PhD in Computer Science Data Science Statistics Mathematics or a related field or a Bachelor s degree with equivalent experience 12 years of experience in data science with a focus on Generative AI e g GPT diffusion models machine learning and data engineering Proficient in Python and key data science libraries TensorFlow PyTorch Scikit-learn Pandas NumPy Hands-on experience with cloud platforms AWS Azure or GCP and containerization Docker Kubernetes GenAI Hands-on with LLMs e g GPT Llama prompt engineering embeddings RAG pipelines vector databases FAISS Milvus Pinecone and LLM evaluation ML DL scikit-learn XGBoost LightGBM PyTorch or TensorFlow experience with time series NLP and recommendation propensity modeling Data Engineering Spark Databricks Delta Lakehouse Airflow or similar orchestration Kafka or equivalent streaming strong grasp of data quality and lineage MLOps LLMOps MLflow or SageMaker Vertex Azure ML feature stores e g Feast Docker Kubernetes CI CD GitHub GitLab monitoring observability for models and LLMs Cloud Proficiency with at least one major cloud AWS Azure GCP including cost performance optimization Expertise in designing and building scalable data pipelines using tools such as Spark Airflow or similar Strong understanding of MLOps practices and deployment of models in production environments Excellent problem-solving analytical and communication skills Desired Skills Experience with natural language processing NLP computer vision or reinforcement learning Familiarity with data visualization tools Tableau Power BI or similar Advanced GenAI agents function calling retrieval fusion long-context strategies PEFT LoRA fine-tuning prompt version governance evaluation suites e g RAGAS promptfoo Security Privacy data anonymization KMS secrets management familiarity with model governance and audit Performance GPU utilization distributed training inference quantization distillation caching token cost control Experience working in Agile or cross-functional teams Patents publications or contributions to open-source AI ML projects Weekly Hours 40 Time Type Regular Location Bangalore India It is the policy of AT T to provide equal employment opportunity EEO to all persons regardless of age color national origin citizenship status physical or mental disability race religion creed gender sex sexual orientation gender identity and or expression genetic information marital status status with regard to public assistance veteran status or any other characteristic protected by federal state or local law In addition AT T will provide reasonable accommodations for qualified individuals with disabilities AT T is a fair chance employer and does not initiate a background check until an offer is made