Top SQL Jobs in Pune

Design, build, and deploy AI-enabled solutions and agentic workflows by integrating enterprise systems, MCP servers, APIs, vector search/RAG, automation tools, and monitoring to solve business problems and deliver measurable outcomes.

Lead architecture and development of scalable software systems, ensuring security and compliance while mentoring engineers and improving system performance.

The Senior Technical Consultant leads customer implementation projects, designs technical solutions, integrates APIs, and provides technical guidance while managing customer relationships.

As an Integration Engineer at Drivetrain, you will manage customer integrations from kick-off to go-live, troubleshoot data issues, build connectors, and advocate for customers while providing technical guidance.

Lead delivery of large-scale full‑stack Java projects across the bank using Agile. Collaborate with stakeholders, manage risks, vendors and resources, ensure quality, security and regulatory alignment, and adopt modern technologies to deliver scalable microservices, front-end features and DevOps practices.

Lead data stewardship for CDD/financial crime data: investigate data quality, lineage, and controls; design ETL pipelines; apply advanced analytics and ML to large datasets; build dashboards and automated reports; translate insights into actionable business and compliance recommendations; advise stakeholders and support governance, risk mitigation, and regulatory readiness.

Lead model risk management team to validate statistical and advanced AI models. Perform hands-on validations, design test plans, assess documentation/code, develop validation methods for generative/agentic AI and unstructured data, mentor staff, advise stakeholders, and support regulatory inquiries.

Migrate a legacy MS Access solution to Mendix, develop Mendix (low-code/pro-code) applications, translate VBA to Mendix/Java, integrate with Snowflake, build Power BI dashboards, support stakeholders, UAT, documentation, and follow enterprise security and SDLC standards.

Manage and implement IAM policies, processes, and technologies (authentication, authorization, SSO, PAM). Conduct IAM audits and incident investigations, develop governance and reporting, and support logging/monitoring (ESaaS, Observe). Coordinate with stakeholders, handle audit evidence and compliance (KRI/KCI), and identify improvement and automation opportunities.

Lead and manage software engineering teams to deliver secure, scalable enterprise SaaS and hybrid platform capabilities. Drive architecture, agile/DevOps/CI/CD and test automation improvements, mentor technical staff, coordinate cross-functional stakeholders, participate in design and code reviews, and communicate risks and progress to leadership to ensure roadmap execution.

Lead the development and deployment of AI solutions, mentor teams, and architect cloud-native systems, primarily using Google Cloud, for scalable applications and AI agents.

As a Senior AI Engineer, you will architect and deploy AI solutions, lead a technical team, integrate AI models, and ensure reliable production systems on Google Cloud Platform while collaborating with cross-functional teams.

Design, develop and deliver scalable Java-based RESTful APIs and microservices using Spring Boot. Collaborate with cross-functional teams, lead technical design, ensure secure coding, unit testing, CI/CD, containerization, and production-ready enterprise solutions.

Design, develop, and execute automated test strategies and frameworks. Create and run automated scripts, collaborate with product and engineering to define acceptance criteria, perform root-cause analysis, and drive test automation across the development lifecycle. Mentor peers, participate in code reviews, and improve CI/CD/DevOps testing practices to ensure software quality and reliable releases.

The Data Engineer - DevOps will build and maintain data systems, manage data pipelines, collaborate with data scientists, and ensure data accuracy and security.

Design, develop, and optimize Oracle databases and PL/SQL code; build scalable schemas and ETL/data pipelines; perform query and PL/SQL performance tuning; ensure security, backup, and recovery; collaborate with architects, analysts, developers, vendors, and stakeholders; participate in code reviews, data modeling, and release management.

The Principal Engineer will lead technical design and implementation for scalable distributed systems, mentor junior members, and ensure the platform's reliability and performance.

The Software Engineer designs, develops, and enhances software solutions. Responsibilities include collaboration with teams, adherence to secure coding practices, and implementing unit testing. The role involves leadership or individual contributions in complex analytical tasks, communication, and stakeholder management.

Build and maintain data pipelines, warehouses and lakes; design architectures for secure, scalable data processing; develop processing algorithms; collaborate with data scientists to deploy ML models; lead or support team members and embed risk, controls and governance.

The Data Analyst role focuses on data analysis, issue investigation, and building data pipelines to drive business recommendations and improvements through advanced analytics and visual reporting.

The Senior Integration Engineer will develop and manage integrations between SaaS applications, support engineering teams, and ensure operational excellence while mentoring junior engineers.

Design, build, test, and maintain scalable SaaS platform features and services. Participate in design and code reviews, troubleshoot production issues, improve reliability/security, mentor junior engineers, and collaborate across product, QA, and operations.

Lead architecture and technical design for enterprise SaaS and hybrid platforms. Drive secure, scalable, reliable implementations across distributed systems, mentor engineers, guide design and code reviews, improve engineering standards (CI/CD, testing, observability), and coordinate with product and cross-functional teams to deliver complex initiatives.

Collaborate with game teams to deliver actionable insights that increase user acquisition, engagement, and monetization. Design and instrument analytics events and queries, run exploratory analyses and competitor analysis, generate reports, and design/interpret A/B tests to evaluate game features and offers.

Design, train, optimize, and productionize deep learning models for speech/audio and computer vision. Build scalable training pipelines, perform model inference optimization, integrate models into production services, monitor performance, and collaborate with software and product teams to deliver low-latency, reliable ML features.