Jobs at Proclink

Design, develop, test, document, and maintain scalable Java features for an enterprise fintech credit decision engine. Analyze ambiguous business requirements, decompose them into technical tasks, implement GraphQL and AWS-based solutions, and ensure quality through unit, integration, and end-to-end testing. Collaborate cross-functionally, participate in code reviews, use AI-assisted development responsibly, and contribute to continuous engineering improvement.

Deliver data and analytics solutions for a U.S. insurance client by defining business problems, conducting exploratory and statistical analysis, building Power BI dashboards, and working with large datasets using SQL, Python, or R. Present actionable insights to stakeholders, ensure regulatory alignment, and collaborate with data engineering and AI/ML teams.

Monitor fraud dashboards, investigate suspicious transactions and accounts, identify fraud patterns, analyze transaction data with SQL and Excel, and support fraud detection strategy development. Build reports and dashboards, evaluate fraud rules and model performance, document findings, and collaborate with Risk, Operations, Compliance, and Product stakeholders to address anomalies and mitigate losses.

Leads and mentors Power BI and reporting-tool developers and data analysts. Designs and deploys advanced Power BI dashboards, builds complex DAX measures, manages data models, and optimizes large-dataset performance. Partners with stakeholders to define requirements, KPIs, and business questions while understanding end-to-end architecture. Oversees Power BI workspace security, data security, and governance compliance.

Designs, develops, administers, and optimizes Adobe Campaign V8 marketing automation solutions. Responsibilities include migrating Adobe Campaign Standard, integrating Treasure Data CDP and enterprise platforms through APIs, building multi-channel email, SMS, and push campaigns, managing workflows and customer journeys, developing responsive HTML emails, monitoring campaign performance, troubleshooting delivery issues, and ensuring data quality, security, compliance, and governance.

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.

Leads the design, development, deployment, evaluation, and optimization of machine learning and deep learning models, including Generative AI and agentic systems. Builds scalable APIs and services, collaborates with data engineering and cross-functional teams, documents technical solutions, drives LLM adoption, and mentors junior engineers. Requires expertise in Python, ML/DL frameworks, data pipelines, MLOps, model evaluation, databases, and RESTful API development.

Build and maintain scalable full-stack enterprise applications using Node.js, React.js, TypeScript, REST APIs, relational databases, and microservices. Design backend services, reusable frontend components, secure integrations, automated tests, CI/CD pipelines, and cloud deployments. Optimize performance, provide production support, troubleshoot issues, participate in architecture and code reviews, and mentor engineers. Collaborate with product, design, QA, DevOps, and engineering teams throughout the software development lifecycle.

Leads the design, development, deployment, and optimization of production-grade AI, machine learning, generative AI, chatbots, and autonomous agents. Builds RAG pipelines and semantic search systems, integrates LLMs with cloud and data platforms, and oversees MLOps, monitoring, security, governance, experimentation, and scalability. Collaborates with engineering, data science, and product teams while leading complex AI projects and teams.

Leads the design, development, and delivery of complex cloud-based data pipelines and scalable data systems. Translates business requirements into technical solutions, optimizes ingestion and transformation for performance and cost, and defines standards for data modeling, governance, reliability, and observability. Provides mentorship and technical leadership to data engineering teams while contributing to data architecture reviews and cross-functional technical decisions.

Designs, develops, optimizes, and maintains Drools/KIE business rules for underwriting and real-time decisioning. Translates credit and risk policies into performant, explainable, auditable rules using DRL, decision tables, and rule templates. Responsibilities include architecture, testing, troubleshooting, production support, deployment, documentation, standards enforcement, and mentoring junior engineers.