Job Description

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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.

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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.

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Key Roles and Responsibilities:

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Lead the design, development, and deployment of advanced Generative AI and ML models for diverse business use cases.

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Architect scalable data pipelines and data engineering solutions to support AI/ML projects.

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Collaborate with business stakeholders, product managers, and engineering teams to translate requirements into actionable data science solutions.

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Architect and develop LLM applications (prompt engineering, RAG, tool use/agents, evaluation, safety guardrails).

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Deliver classical ML and deep learning solutions for forecasting, anomaly detection, churn/CLV, propensity, fraud/spam, NLP, and recommendation systems.

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Optimize models for performance, latency, and cost (quantization, distillation, caching).

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Mentor and guide junior data scientists and data engineers, fostering a culture of technical excellence and continuous learning.

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Ensure best practices in model development, version control, model monitoring, and documentation.

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Develop and enforce data governance, security, and compliance standards within data science initiatives.

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Communicate complex technical concepts and results to both technical and non-technical stakeholders.

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Drive the adoption of MLOps and automation tools to streamline the model lifecycle.

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Evaluate and implement tools, frameworks, and infrastructure to enhance data science capabilities.

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Stay current with the latest research and advancements in AI/ML and assess their applicability to business challenges.

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Required Skills:

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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.

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12+ years of experience in data science, with a focus on Generative AI (e.g., GPT, diffusion models), machine learning, and data engineering.

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Proficient in Python and key data science libraries (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).

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Hands-on experience with cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes).

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GenAI: Hands-on with LLMs (e.g., GPT, Llama), prompt engineering, embeddings, RAG pipelines, vector databases (FAISS/Milvus/Pinecone), and LLM evaluation.

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ML/DL: scikit-learn; XGBoost/LightGBM; PyTorch or TensorFlow; experience with time series, NLP, and recommendation/propensity modeling.

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Data Engineering: Spark/Databricks, Delta/Lakehouse; Airflow or similar orchestration; Kafka or equivalent streaming; strong grasp of data quality and lineage.

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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.

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Cloud: Proficiency with at least one major cloud (AWS/Azure/GCP), including cost/performance optimization.

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Expertise in designing and building scalable data pipelines (using tools such as Spark, Airflow, or similar).

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Strong understanding of MLOps practices and deployment of models in production environments.

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Excellent problem-solving, analytical, and communication skills.

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Desired Skills:

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Experience with natural language processing (NLP), computer vision, or reinforcement learning.

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Familiarity with data visualization tools (Tableau, Power BI, or similar).

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Advanced GenAI: agents/function calling, retrieval fusion, long-context strategies, PEFT/LoRA fine-tuning, prompt/version governance, evaluation suites (e.g., RAGAS, promptfoo).

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Security/Privacy: data anonymization, KMS, secrets management; familiarity with model governance and audit.

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Performance: GPU utilization, distributed training/inference, quantization, distillation, caching/token cost control.

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Experience working in Agile or cross-functional teams.

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Patents, publications, or contributions to open-source AI/ML projects.

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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.