Role Description - Design, develop, and maintain scalable data pipelines using Python and PySpark - Build and optimize batch and near‑real‑time data processing workflows - Work on GCP‑based data platforms, integrating multiple structured and semi‑structured data sources - Develop data transformations, validations, and enrichment logic aligned to business requirements - Optimize Spark jobs for performance, scalability, and cost efficiency - Collaborate with data architects, platform teams, and downstream consumers - Implement logging, monitoring, and error‑handling within data pipelines - Ensure adherence to enterprise data governance, security, and compliance standards - Support production deployments and provide L2/L3 support as needed Company Description At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus. Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself. We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.

• Define and evolve enterprise data and AI architecture strategy, roadmaps, standards, and reference architectures • Lead architecture for enterprise data platforms, analytics ecosystems, customer intelligence capabilities, and AI-enabled solutions • Establish semantic data models, enterprise ontologies, metadata standards, and knowledge architectures • Partner with Product, Engineering, Data Science, and Analytics teams to deliver AI-ready data foundations • Drive architecture governance, data quality, security, scalability, and compliance across enterprise data and AI platforms • Evaluate emerging technologies and provide technical leadership for strategic investments in data, analytics, knowledge management, and AI capabilities

• Lead, mentor, and grow a distributed analytics team across multiple time zones • Establish priorities, set goals, and manage performance to foster a high-performing analytics organization • Develop and implement analytics standards, best practices, and processes to ensure quality and consistency • Architect, build, and maintain scalable ETL/ELT pipelines, event streams, and ingestion frameworks for analytics, product features, and ML applications • Oversee the design and implementation of cloud-native data infrastructure, including data warehouses, lakes, transformation layers, and orchestration • Ensure data quality, consistency, governance, and observability across data systems • Oversee and guide dashboards, automated reports, and analytics tools using BI platforms such as Tableau, Looker, and Power BI • Ensure robust data pipelines, reliable metrics, and scalable reporting solutions in collaboration with data engineering • Prioritize analytics projects, balance roadmap demands, and ensure timely delivery • Champion analytical excellence, including experiment design, KPI definition, and model validation • Partner with product, analytics, and business leaders to ensure data is accurate, accessible, and scalable • Strengthen analytics capabilities and deliver actionable insights that drive organizational decision-making

Role Description - Design and develop production-ready ML and LLM applications using Python. - Build scalable ML pipelines on Databricks and AWS. - Implement LLMOps for deployment, monitoring, evaluation, and model lifecycle management. - Develop cluster and job orchestration for large-scale processing. - Build automated CI/CD pipelines for ML workloads. - Develop NLP solutions for structured and unstructured healthcare data. - Optimize scalable compute orchestration to eliminate processing bottlenecks. - Collaborate with architects, data engineers, and QA teams. Qualifications - Python - Machine Learning Engineering (MLE) - LLMOps - Databricks - AWS - SQL - Natural Language Processing (NLP) - Cluster & Job Orchestration - Scalable Compute Orchestration - Automated CI/CD Pipelines - Git Requirements - MUST HAVE - PHI / HIPAA-compliant data handling practices - HL7 ADT message format knowledge - Recent experience in Healthcare data domain - NICE TO HAVE - MLflow - Docker/Kubernetes - FHIR - RAG - Vector Databases Benefits - 401(k) matching - Dental insurance - Health insurance