Ts Ii cloud delivery engineer Jobs – 54 Open Jobs Today

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Manager II, Tech(AI)

RealPage, Inc. -
Ranga Reddy District, Telangana

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Job details

Qualifications

BCS
CI/CD
Azure
Law
Doctoral degree
Computer Science
Spark
Java
Master's degree
SQL
AWS
Bachelor's degree
Scala
Continuous integration
APIs
Kafka
Data science
Redshift
AI
Leadership
Communication skills
Graph databases
Python
Data Science

Full job description

Overview:

BEST is modernizing our data capabilities to accelerate product innovation, data-driven decision making, and AI-enabled experiences. Today the organization operates with traditional data engineering practices; we are hiring a transformation leader who will modernize the platform, build production-grade ML/AI capabilities, and embed responsible AI across the business.

We’re hiring a strategic, hands-on Sr. Manager, Data Services to own and scale BEST’s data platform and teams—leading engineers and managers across ingestion, transformation, platform services, data products/APIs, governance, and production ML/AI. You’ll be accountable for MLOps, model serving, and the people/processes to safely operationalize AI at scale, transforming a traditional data-engineering function into an AI-first, product-oriented organization. This role requires deep technical expertise in data platforms, MLOps and LLM/AI patterns, plus proven people leadership and cross-functional influence.

Responsibilities:

Strategy & Roadmap — Define and operationalize a 12–24 month roadmap for Data Services that supports company data and AI goals (data APIs, platform, governance, ML pipelines, model serving and monitoring).
Team Leadership — Hire, mentor and grow a high-performing team (data engineers, platform engineers, ML engineers, SREs). Build leadership bench and establish clear career paths.
Data Platform Ownership — Own the design, delivery, and run-book for data platforms (data lake/warehouse, streaming, ingestion, processing, metadata/catalog, lineage and observability).
ML/AI Enablement — Partner with AI/ML teams to build repeatable ML pipelines and production model serving (batch & real-time). Lead MLOps adoption: model training, deployment, CI/CD, drift detection, and model governance.
AI Productionization — Drive production-grade implementations of AI use-cases (including large language models, retrieval-augmented generation, knowledge graphs, agentic/automated workflows). Ensure safe, scalable, and cost-efficient model serving and inference.
Data Products & APIs — Deliver production data products and APIs consumed by analytics, product teams, and AI systems. Enable self-service data and ML capabilities for downstream teams.
Quality, Observability & Governance — Implement enterprise-grade data quality, lineage, cataloging, access controls, and privacy/compliance practices. Partner with security and legal on policy for AI model usage and data privacy.
Stakeholder Management — Work cross-functionally with product, engineering, marketing, sales, and COE to translate business needs into data and AI programs, prioritizing high-impact work and measuring outcomes.
Metrics & Outcomes — Define and report KPIs for platform reliability, time-to-insight, model accuracy/performance, cost, and adoption.

Qualifications:

12+ years’ experience in data engineering, data platform, analytics, or ML engineering roles, with at least 3+ years in a people-/team-management role.
Hands-on experience building and operating large-scale data platforms (data lakes/warehouses, Spark/Databricks, Snowflake/Redshift/BigQuery, streaming with Kafka/Kinesis).
Demonstrated track record of taking ML/AI projects from POC to production. Practical experience with MLOps tooling and pipelines (e.g., MLflow, Kubeflow, SageMaker, or equivalent).
Deep familiarity with model serving and inference architectures (real-time and batch), model monitoring, automated retraining, and drift detection.
Strong engineering skills: Python / SQL (and/or Scala/Java), data pipeline frameworks, and versioned data/model workflows.
Experience with LLMs and modern AI application patterns: prompt engineering, retrieval-augmented generation (RAG), knowledge graphs, LLM safety/guardrails, and LLMOps frameworks (e.g., LangChain, LlamaIndex).
Cloud platform experience (AWS / Azure / GCP) and experience designing cost-efficient, production-grade ML inference at scale.
Experience with data governance, cataloging (Amundsen/Atlas/Data Catalog), lineage and privacy compliance (GDPR/CCPA) in a production environment.
Excellent people and cross-functional leadership skills, with strong communication, stakeholder influence, and program management ability.
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field. Advanced degree (MS/PhD) in a quantitative or engineering discipline preferred.

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ii cloud delivery engineer jobs in ts

Manager II, Tech(AI)

Databricks Practice Lead / Architect

SW Engineer II

Platform Engineer II – Compute

Engineer II, Software Development

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