• Independently solves complex, ambiguous business problems and brings new perspectives to existing solutions across operational, financial, and clinical domains. • Structures and leads advanced analytical and AI-driven approaches (e.g., predictive modeling, machine learning, optimization, forecasting, and emerging AI applications) to address high-impact business challenges. • Partners closely with business stakeholders to understand objectives, constraints, and success metrics; translates these into scalable analytical solutions and actionable recommendations. • Acts as an analytics product owner by defining problem statements, shaping solution approaches, prioritizing workstreams, and driving measurable business outcomes. • Leads development of moderately complex solutions with meaningful business risk and visibility, ensuring rigor, explainability, and operational scalability. • Develops and delivers executive-ready presentations that clearly communicate technical findings, trade-offs, and recommendations to senior leadership and client-facing teams. • Applies strong knowledge of statistical methods, machine learning algorithms, and AI techniques to detect patterns, assess risk, and generate business insights. • Uses advanced programming skills to explore, transform, and analyze large and complex datasets from multiple internal and external sources. • Demonstrates strong consulting skills, including proactive problem identification, stakeholder alignment, and the ability to influence decision-making in cross-functional environments.

• Architect, train, and deploy generative AI models, including large language models (LLMs) and transformer-based systems. • Develop robust predictive and optimization models using structured and unstructured data sources. • Implement and deploy machine learning models using AWS SageMaker for scalable and efficient performance in a cloud environment. • Partner with cross-functional teams to translate business requirements into scalable AI solutions. • Drive model lifecycle management from research and experimentation through deployment and monitoring. • Maintain high standards for model governance, explainability, performance, and ethical AI. • Stay at the forefront of GenAI advancements and proactively evaluate new tools, methods, and frameworks.

• Define technical investments with business objectives • Mentor, and manage AI/ML engineers, senior data scientists, and MLOps engineers—setting performance expectations and a high-performance culture. • Partner with cross-functional leaders to prioritize initiatives, allocate resources, and measure organizational impact. • Establish engineering standards, code review practices, and model governance frameworks across the AI org. • Serve as the technical authority on deep learning architecture—personally leading the design and development of custom transformer models for sequence modeling, customer propensity scoring, audience segmentation, and churn prediction. • Drive innovation in attention mechanisms, positional encodings, and tokenization strategies specifically suited to tabular, time-series, and event-stream data common in marketing and telecom. • Oversee adaptation and fine-tuning of foundation models (BERT, T5, TabTransformer, LLMs) for proprietary client datasets, ensuring domain-specific performance. • Champion reproducible experimentation and architectural decision documentation across the team. • Oversee end-to-end data science workflows: problem framing, feature engineering, model development, validation, and production deployment. • Ensure statistical rigor in experimental design, causal inference, A/B testing, and offline/online evaluation frameworks. • Guide the team in building robust data pipelines for large-scale structured and unstructured datasets, including clickstream, CRM, ad telemetry, CDRs, and network KPIs. • Lead technical discovery and solutioning with enterprise clients translating ambiguous business problems into well-scoped AI initiatives. • Present AI strategy, model results, and roadmap updates to C-suite and senior client stakeholders with clarity and executive presence. • Contribute to business development: support RFP responses, lead technical portions of client proposals, and help grow the AI engineering practice. • Establish production standards for model deployment, monitoring, drift detection, and automated retraining across cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML). • Drive adoption of MLOps best practices including CI/CD for ML, containerization (Docker/Kubernetes), and experiment tracking (MLflow, W&B, DVC). • Implement model governance, explainability, and responsible AI standards in compliance with client and regulatory requirements.

• Support and manage highly complex software development initiatives, including data engineering, data analysis, user support, and enterprise-scale data solutions • Manage and drive integrated teams, delivering high-quality technology solutions across big data platforms • Designing, developing, and maintaining highly scalable ETL applications on Hadoop/Spark environments • Delivering big data projects using Spark, Python, Scala, SQL, and Hive within distributed computing ecosystems • Leading and mentoring onshore and offshore development teams, ensuring high-quality delivery and consistent engineering standards • Actively participating in—and at times leading—Agile/Scrum ceremonies, including daily stand-ups, sprint planning, retrospectives, and code reviews • Tuning and optimizing ETL pipeline performance to meet strict SLAs across highly complex, concurrent processing environments • Engaging with business partners to understand needs, translate requirements, and identify appropriate technology solutions • Coordinating development project execution from initiation through release, including dependency planning and sprint scheduling • Managing team velocity, financials, and other critical delivery metrics • Reviewing product backlog, designs, code, and test results to mitigate delivery risks and ensure product quality • Leading adoption of innovative technologies to support evolving software product and data platform needs • Assessing, installing, and enhancing software required for the organization’s broader big data platform • Building robust ETL pipelines supporting day-to-day client delivery operations