3 Graphdbs Jobs

What you’ll doAs a Data Engineer – Data Platform Services, you will be responsible for: Data Migration & Modernization Leading the migration of ETL workflows from IBM DataStage to PySpark, ensuring performance optimization and cost efficiency. Designing and implementing data ingestion frameworks using Kafka and PySpark, replacing legacy ETL Pipeline using DataStage. Migrating the analytical platform from IBM Integrated Analytics System (IIAS) to Cloudera Data Lake on CDP. Data Engineering & Pipeline Development Developing and maintaining scalable, fault-tolerant, and optimized data pipelines on Cloudera Data Platform. Implementing data transformations, enrichment, and quality checks to ensure accuracy and reliability. Leveraging Denodo for data virtualization and enabling seamless access to distributed datasets. Performance Tuning & Optimization Optimizing PySpark jobs for efficiency, scalability, and reduced cost on Cloudera. Fine-tuning query performance on Iceberg tables and ensuring efficient data storage and retrieval. Collaborating with Cloudera ML engineers to integrate machine learning workloads into data pipelines. Security & Compliance Implementing Thales CipherTrust encryption and tokenization mechanisms for secure data processing. Ensuring compliance with Bank/regulatory body security guidelines, data governance policies, and best practices. Collaboration & Leadership Working closely with business stakeholders, architects, and data scientists to align solutions with business goals. Leading and mentoring junior data engineers, conducting code reviews, and promoting best practices. Collaborating with DevOps teams to streamline CI/CD pipelines, using GitLab and Nexus Repository for efficient deployments. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 12+ years of experience in Data Engineering, ETL, and Data Platform Modernization. Hands-on experience in IBM DataStage and PySpark, with a track record of migrating legacy ETL workloads. Expertise in Apache Iceberg, Cloudera Data Platform, and Big-data processing frameworks. Strong knowledge of Kafka, Airflow, and cloud-native data processing solutions. Experience with Denodo for data virtualization and Talend DQ for data quality. Proficiency in SQL, NoSQL, and Graph DBs (DGraph Enterprise). Strong understanding of data security, encryption, and compliance standards (Thales CipherTrust). Experience with DevOps, CI/CD pipelines, GitLab, and Sonatype Nexus Repository. Excellent problem-solving, analytical, and communication skills. Preferred technical and professional experience Experience with Cloudera migration projects in Banking or financial domains. Experience working with Banking Data model. Knowledge of Cloudera ML, Qlik Sense/Tableau reporting, and integration with data lakes. Hands-on experience with QuerySurge for automated data testing. Understanding of code quality and security best practices using CheckMarx. IBM, Cloudera, or AWS/GCP certifications in Data Engineering, Cloud, or Security. “Meghdoot” Cloud platform knowledge. Architectural designing and recommendations the best possible solutions.

AI Engineer Partner Ecosystem: Roles and Responsibilities An AI Engineer focused on the partner ecosystem works at the intersection of artificial intelligence (AI), business partnerships, and technical enablement. This role is responsible for driving AI adoption, enabling partners with AI tools, and collaborating on AI-driven solutions. Below are the key responsibilities: 1. Partner Enablement & AI Adoption Educate partners on AI technologies, frameworks, and best practices. Conduct workshops, webinars, and training sessions to upskill partners on AI models and tools. Provide technical guidance and resources to help partners integrate AI into their solutions. Support partners in understanding AI/ML development, deployment, and optimization. 2. AI Solution Development & Integration Collaborate with partners to design, develop, and implement AI-powered solutions. Assist partners in integrating AI models (e.g., machine learning, NLP, computer vision) into their products. Support the customization of AI models for partner use cases. Ensure scalability, security, and efficiency in AI deployments within partner ecosystems. 3. Technical Support & Troubleshooting Provide hands-on technical support for partners during AI development and deployment. Troubleshoot AI-related issues, ensuring smooth integration with partner platforms. Work closely with internal AI teams to address partner challenges. 4. Collaboration & Relationship Management Build and maintain strong relationships with technology and business partners. Act as a bridge between internal AI teams and external partners. Gather feedback from partners and relay insights to improve AI offerings. Align AI solutions with partner business objectives and industry trends. 5. AI Research & Innovation Stay updated on the latest AI trends, tools, and best practices. Experiment with emerging AI models and frameworks to provide innovative solutions for partners. Collaborate with AI researchers and developers to enhance AI capabilities. 6. Compliance, Ethics & Responsible AI Ensure AI solutions comply with ethical AI principles and industry regulations. Advise partners on AI governance, data privacy, and compliance requirements. Promote fairness, transparency, and accountability in AI deployments. Required education Bachelor's Degree Preferred education Bachelor's Degree Required technical and professional expertise AI & Machine Learning Frameworks Proficiency in AI/ML libraries Experience working with large-scale AI models (e.g., LLMs, generative AI, reinforcement learning). Knowledge of edge AI and AI model optimization for different deployment environments. Programming & Development Skills Strong coding skills in Python Experience with AI-driven APIs and SDKs for partner integrations. Knowledge of DevOps/MLOps pipelines, CI/CD for AI, and automation tools. Experience with AI/ML services from major cloud providers:Familiarity with Kubernetes, Docker, and serverless AI deployments. 4. Data Engineering & AI Infrastructure Knowledge of big data processing frameworks (Apache Spark, Hadoop, Databricks). Experience with data pipelines, feature engineering, and model training at scale . Understanding of database technologies (SQL, NoSQL, GraphDBs). 5. AI Analytics & Monitoring Familiarity with AI observability and monitoring tools ( Experience in model explainability and AI performance tuning. Preferred technical and professional experience Strong experience in AI/ML, deep learning, and data science including Generative AI Proficiency in Python, TensorFlow, PyTorch, or other AI frameworks. Knowledge of cloud AI services Experience with MLOps, AI deployment, and model optimization. Excellent communication and technical enablement skills. Ability to work cross-functionally with internal teams and external partners.

Sr. Java Developer Key Responsibilities: Develop and maintain Java/J2EE applications using Spring Boot framework. Write efficient SQL queries and optimize database performance. Containerize applications using Docker for deployment and scalability. Collaborate with cross-functional teams to design, develop, and implement solutions. Troubleshoot and debug issues, ensuring smooth operation of applications. Stay updated with the latest technologies and industry trends. GraphDB like Neo4J exposure Requirements: Bachelor's degree in Computer Science or related field. 4-8 years of hands-on experience in Java/J2EE development. Proficiency in Spring Boot framework, Microservices and related technologies. Strong knowledge of SQL and database design principles. Experience with Docker or other containerization technologies. Excellent communication skills and ability to collaborate effectively with team members. Understanding of YAML configuration files. Telecom OSS knowledge is a plus. Please share your cv on 9274416061 (Whats app only)