Top Tech Jobs & Startup Jobs in Mumbai

Leads enterprise architecture strategy for packaged and SaaS applications, overseeing solution design, configuration, integrations, performance, and releases. Advises leadership, drives enterprise-wide architectural decisions, evaluates emerging AI and cloud technologies, and develops scalable multi-cloud solutions. The role requires hands-on programming expertise, AI-native development and AI-led SDLC experience, knowledge-base integration, cross-team collaboration, and mentorship of junior engineers.

Investigate, troubleshoot, and resolve issues across critical business applications. Monitor system performance, analyze logs, identify root causes, implement fixes, and coordinate with development and operations teams. The role serves as a subject matter expert, manages team decisions, collaborates cross-functionally, mentors junior engineers, and proactively reduces application downtime and operational risk.

Design, develop, and maintain data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, support data integration and migration across systems, and apply data modeling, governance, and database design practices. Collaborate with multiple teams, make technical decisions, mentor junior engineers, and improve data processes. The role requires expertise in Snowflake and leadership as a data engineering subject matter expert.

Designs, develops, and maintains data solutions, pipelines, and ETL processes using Snowflake. Ensures data quality, supports data migration across systems, applies data modeling and governance practices, and collaborates with multiple teams. The role serves as a subject matter expert, makes team decisions, solves cross-team data problems, and mentors junior data engineers.

Design, develop, and maintain data solutions, pipelines, and ETL processes across systems. Ensure data quality, support data modeling and governance, and collaborate with multiple teams on data requirements and technical decisions. Serve as a subject matter expert, manage team activities, mentor junior engineers, and provide solutions to cross-team data engineering challenges.

Design, develop, and maintain data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, support data migration across systems, and apply data modeling, querying, and governance practices. Collaborate with multiple teams, make technical decisions, provide solutions, and mentor junior data engineers. The role requires at least five years of Snowflake experience and 15 years of full-time education.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, data governance, and effective integration across systems. Collaborate with multiple teams, provide technical solutions, make team decisions, mentor junior engineers, and improve data processes. The role requires expertise in data modeling, database design, cloud-based data processing, and Snowflake data warehousing.

Develop and maintain Snowflake-based data warehousing solutions, infrastructure, databases, pipelines, and ETL processes. Design scalable applications, write complex SQL and Python scripts, implement data security controls, and collaborate with analysts and stakeholders. The role requires independent execution, subject matter expertise, and ongoing improvement of data analysis and reporting capabilities.

Design, develop, test, deploy, maintain, and enhance enterprise software using Python, Generative AI, and Agentic AI. Lead AI-driven solution delivery, apply prompt engineering and evaluation frameworks, troubleshoot complex issues, collaborate across teams, make technical decisions, mentor junior engineers, and improve development processes. The role requires expertise in software methodologies, Git, automated testing, and responsible, scalable AI implementation.

Design and deploy enterprise agentic AI architectures and multi-agent systems using Microsoft AI Foundry, Semantic Kernel, AutoGen, Copilot Studio, and Azure AI services. Lead technical decisions, integrate LLMs and SLMs, implement Responsible AI governance, optimize solution performance, collaborate with engineering and product teams, and mentor teams on AI system design and deployment.