Top Tech Jobs & Startup Jobs

Lead refinery and petrochemical planning and scheduling solution engagements, including requirements analysis, functional and technical design, linear programming model updates, network architecture, implementation, and team leadership. Requires expertise in refinery planning and scheduling tools, plant economics, blending, production processes, business-process integration, and customer-site delivery. Candidates need chemical engineering or oil and gas qualifications and 5–15 years of relevant experience.

Designs, builds, and maintains Azure-based data solutions and ETL pipelines using Azure Data Factory and Databricks. Manages SQL and MySQL databases, develops data models, optimizes queries, supports data governance and quality, and documents architectures and pipelines. Collaborates with data scientists, analysts, and business stakeholders to deliver reliable data for analysis and reporting. The role also involves security compliance and may include real-time processing, multi-cloud technologies, and machine learning concepts.

Leads a small analytics team while analyzing complex business data, writing SQL queries, developing dashboards and MIS reports, tracking KPIs, validating data, and delivering actionable recommendations. Partners with business, product, operations, and technology teams to translate requirements into analytical solutions, automate recurring reports, and communicate insights to technical and non-technical stakeholders.

Designs, develops, and supports scalable Azure data platforms using Databricks, ADLS Gen2, PySpark, SQL, Delta Lake, and Azure Data Factory. Builds batch, incremental, CDC, and near-real-time pipelines; optimizes Spark workloads, storage, and cloud costs; implements data quality, governance, security, CI/CD, and monitoring. Provides production L2/L3 support, resolves incidents, maintains documentation, and collaborates with cross-functional engineering and architecture teams.

Develop and support Azure-based integration solutions using C#, .NET Core, REST APIs, Azure PaaS, and SQL Server. Responsibilities include requirements analysis, solution design, coding, unit testing, defect resolution during QA and UAT, and production support at go-live. The developer will work with Logic Apps, Azure Functions, API Management, Application Insights, Key Vault, App Services, Entity Framework Core, and potentially Blazor and Azure Integration Services.

Supports backend API and service development, frontend integrations, unit testing, code reviews, and Agile/Scrum collaboration. The trainee will translate business requirements into technical tasks, apply coding and scalability practices, and learn software development tools and technologies. Required knowledge includes object-oriented programming, data structures, programming languages, databases, SQL, REST APIs, authentication, and web fundamentals. Preferred exposure includes frontend or backend frameworks, cloud platforms, containers, Kubernetes, microservices, and GraphQL.

Designs enterprise-grade agentic AI architectures, including reasoning, planning, memory, knowledge integration, tool/API layers, and lifecycle management. Leads framework selection, prototypes, technical standards, MLOps and LLMOps strategies, security, compliance, and architectural documentation. Provides senior technical guidance and mentorship while aligning agentic AI solutions with business objectives across supply chain, automation, ERP, CRM, SCM, and WMS environments.

Build and maintain scalable backend systems for a credit card platform using Node.js, GraphQL, TypeScript, and REST APIs. Design microservices, optimize performance, ensure code quality through testing and automation, collaborate across teams, mentor junior developers, and improve development and operational processes. The role requires cloud-native application experience, with AWS preferred, and familiarity with DevOps, Agile, Kubernetes, and CI/CD practices.

Leads the design, development, and optimization of large-scale cloud data pipelines and ETL/ELT systems. Builds infrastructure for collecting, transforming, and distributing customer data while ensuring quality, security, scalability, and performance. Collaborates with architects, analysts, and stakeholders, mentors engineers, researches new tools, and supports DevOps through CI/CD and infrastructure-as-code practices.

Build, tune, deploy, and validate machine learning models for fraud detection, risk scoring, and customer analytics in the BFSI and credit card domain. The role involves performance and stability testing, documentation, troubleshooting data and model issues, communicating findings to varied stakeholders, managing analytical projects independently, and staying current with emerging AI and ML techniques.