Job Overview

We are seeking an experienced Backend Java Developer with strong hands-on expertise in Java 8/9, Core Java, backend integrations, and modern engineering practices. The ideal candidate should have solid design pattern knowledge, experience working on enterprise-grade integrations, and exposure to cloud and DevOps tools.

Primary Skills (Must Have)

• Java 8/9, Core Java

• Design Patterns (beyond Singleton/Factory)

• Web Services Development (REST & SOAP)

• XML & JSON processing

• CI/CD pipelines

Secondary Skills (Good to Have)

• Jenkins

• Kubernetes

• Google Cloud Platform (GCP)

• SAP JCo Library

Optional Certifications: OCPJP, Google Professional Cloud Developer

Required Experience

• Strong experience in integration component development using Java 8/9 & SOA

• Deep knowledge of integration architecture and design patterns

• Hands-on experience with tools such as Eclipse, NetBeans, JIRA, Confluence, Bitbucket, SVN

• Exposure to modern IT trends: Cloud Platforms, DevOps, Low-Code, Intelligent Automation

• Experience with GCP is an added advantage

• Preferably worked on 3–4 integration adapters or connectors for ERP/CRM/HCM/SCM/Billing applications

Behavioral Competencies

• Experience working with US/Europe clients in onsite–offshore delivery model

• Strong verbal & written communication, documentation, and presentation skills

• Excellent analytical and problem-solving abilities

• Effective task prioritization, time management, and stakeholder management

• Quick learner and strong team player

• Ability to work under tight deadlines in a matrix environment

• Demonstrated Organizational Citizenship Behavior (OCB)

Job Responsibilities

• Write design specifications and user stories for assigned modules

• Develop backend components/classes and support QA teams with test cases

• Maintain coding best practices and conduct peer code reviews

• Participate in Scrum ceremonies (Daily Standup, Planning, Retro, Demos)

• Identify technical/design/architecture risks and create mitigation plans

• Adhere to prescribed development processes and documentation standards

• Collaborate with Architects and cross-functional teams on technical issues, demos, POCs, and proposals

• Contribute to internal knowledge repositories, reusable accelerators, and IP creation

• Mentor junior developers and provide constructive feedback

• Deliver internal training on new technologies

• Participate in organizational initiatives—interviews, CSR activities, events, policy implementation

Responsibilities: 

• Install, configure, and maintain Confluent Kafka clusters in an on-premises environment. 

• Manage Kafka brokers, Zookeeper, Connect, Kafka Streams, and related services. 

• Monitor and tune the performance of Kafka clusters to meet operational SLAs. 

• Implement and manage Kafka security including SSL/TLS, ACLs, role-based access control (RBAC). 

• Ensure that Kafka infrastructure complies with the organization's security policies and best practices. 

• Set up monitoring and alerting for Kafka clusters using tools like Prometheus, Grafana, or Confluent Control Center. 

• Analyze logs and metrics to proactively detect and resolve issues. 

• Develop automation scripts and frameworks to streamline Kafka operations, such as provisioning, configuration, scaling, and monitoring using tools like Ansible, Terraform, or custom Python scripts. 

• Automate Kafka cluster deployments, upgrades, and patch management. 

• Implement auto-scaling, failover, and disaster recovery solutions through automation. 

• Knowledge of CI/CD pipelines and infrastructure-as-code (IaC) concepts for Kafka deployments.

• Regularly perform maintenance and upgrades for Kafka components (brokers, Zookeeper, etc.). 
• Handle Kafka backup and disaster recovery procedures. 

• Provide operational support and incident management for Kafka-related issues. 

• Work closely with developers and data engineers to resolve application-level issues. 

• Maintain accurate documentation of Kafka architecture, configuration, and operational procedures. 

• Create runbooks for various Kafka administrative tasks. 

• 3-5 years of hands-on experience managing Confluent Kafka clusters in production environments, preferably on-premises. 
• Solid understanding of distributed systems, high availability, and failover mechanisms. 
• Experience with Kafka Cluster Linking and cross-cluster replication. 

• Remote Working position – preference for resource to be based in US. 

• At least six hours coverage for US CDT – for resources based in India. 

• Travel required – At start of the engagement  

Role Overview: 

Key Responsibilities: 

• Lead the design and implementation of data models and pipelines supporting marketing analytics and customer intelligence. 

• Integrate and manage data from Customer Data Platforms (CDPs), CRM, and marketing automation tools to create a unified customer view. 

• Leverage Agentic AI and Gen AI frameworks to automate analytical workflows and drive advanced insights. 

• Analyze marketing performance across channels to measure ROI, customer engagement, retention, and lifetime value. 

• Build scalable dashboards, reports, and visualizations for marketing and executive stakeholders using tools like Tableau, Power BI, or Looker. 

• Develop and apply machine learning models for segmentation, churn prediction, personalization, and recommendation systems. 

• Drive experimentation and A/B testing to evaluate marketing effectiveness and optimize performance. 

• Ensure data quality, governance, and compliance across data systems, analytics platforms, and pipelines. 

• Partner with Marketing, Product, Data Science, and Engineering teams to define KPIs, optimize customer journeys, and enhance customer loyalty initiatives. 

• Translate analytical findings into strategic recommendations to improve marketing performance and business outcomes. 

Requirements: 

• 6–8 years of experience in data analytics, marketing analytics, or data science, with a strong technical foundation. 

• Proven experience working with Customer Data Platforms (CDP) and marketing data ecosystems. 

• Hands-on expertise with Adobe Analytics, Google Analytics, and related CRM/data management platforms. 

• Advanced proficiency in SQL and experience working with large-scale datasets and cloud environments. 

• Strong programming skills in Python or R for statistical analysis, modeling, and data processing. 

• Deep understanding of marketing attribution, ROI modeling, and customer journey analytics. 

• Experience in data visualization, dashboarding, and storytelling through data. 

• Strong foundation in statistical testing, A/B testing, and experiment design. 

Preferred Qualifications: 

• Experience with cloud-based data platforms (e.g., Snowflake, BigQuery, AWS, GCP). 

• Hands-on experience in CDP configuration, audience segmentation, and data activation workflows. 

• Knowledge of Agentic AI applications, predictive modeling, and real-time personalization frameworks. 

• Background in consumer-centric industries such as retail, eCommerce, or financial services. 

• Strong understanding of data governance, metadata management, and data quality frameworks. 

Role Overview: 

Key Responsibilities: 

• Analyze marketing performance across channels using advanced data techniques. 

• Build dashboards and reports to monitor KPIs such as customer acquisition, conversion, engagement, and ROI. 

• Develop and apply machine learning models for customer segmentation, churn prediction, recommendation systems, and lifetime value estimation. 

• Design and evaluate A/B tests and experiments to support marketing optimization. 

• Partner with cross-functional teams (Marketing, Product, Data Engineering, Data Science) to define metrics and improve campaign targeting and personalization. 

• Translate complex analytical findings into clear business insights and recommendations. 

• Ensure high standards of data quality, integrity, and governance across tools and systems. 

Requirements: 

• 6–8 years of experience in marketing analytics, data science, or related field. 

• Proven experience applying AI/ML techniques to marketing problems (e.g., clustering, classification, regression, recommendation engines). 

• Advanced proficiency in SQL and experience working with large datasets. 

• Strong programming skills in Python or R for data analysis and modeling. 

• Familiarity with marketing platforms and tools (e.g., Google Analytics, Adobe Analytics, CRM systems). 

• Proficiency in data visualization tools (e.g., Tableau, Power BI, Looker). 

• Solid understanding of marketing funnels, attribution, and customer journey analytics. 

• Experience with statistical testing, experimentation, and A/B testing. 

Preferred Qualifications: 

• Experience working with cloud-based data platforms (e.g., Snowflake, BigQuery, AWS, GCP). 

• Exposure to CDPs, marketing automation platforms, and real-time data pipelines. 

• Background in consumer-facing industries such as retail, eCommerce, or financial services. 

• Knowledge of predictive modeling and personalization frameworks. 

About the Role: 

Key Responsibilities: 

• Sales Leadership:Own and drive the sales strategy for technical and consulting services, including AI-based solutions, across India. 

• Lead Generation & Pipeline Building:Proactively identify, qualify, and develop new business opportunities through a combination of outbound prospecting, partner channels, events, and inbound marketing leads. 

• Client Engagement:Build and nurture relationships with key decision-makers (CXO level) of existing customers across industries to understand their business needs and position our services as strategic solutions. 

• Pan-India Expansion:Manage and grow a sales pipeline across regions with a focus on enterprise accounts, ensuring consistent YoY growth. 

• Solution Selling:Lead consultative sales engagements, working closely with technical teams to craft tailored proposals and value propositions. 

• Partnership & Alliances:Collaborate with technology partners, AI platform providers, and system integrators to expand our solution portfolio. 

• Team Collaboration:Work closely with delivery, pre-sales, and product teams to ensure successful project handoffs and customer satisfaction. 

• Sales Forecasting & Reporting:Maintain accurate sales forecasts and provide regular updates to senior management. 

Requirements: 

• Experience:Minimum 5+ years in technical or consulting services sales, preferably with exposure to AI/ML technologies, cloud services, or enterprise digital transformation projects. 

• Domain Expertise:Strong understanding of AI-based services and solutions (e.g., AI consulting, AI product implementation, automations, data platforms). 

• Geography:Must be based in the Delhi NCR region and open to travel across India as needed. 

• Sales Acumen:Demonstrated ability to close complex, high-value deals and manage long sales cycles. 

• Communication:Excellent presentation, negotiation, and stakeholder management skills. 

• Travel:Willingness to travel up to 50%of the time across India for client meetings and events. 

Preferred Qualifications: 

• Experience working with Indian IT consulting firms. 

• Familiarity with enterprise AI adoption trends and technologies. 

• Existing client relationships in BFSI, Manufacturing, Retail, or Public Sector. 

Compensation: 

• Competitive base salary 

• Attractive commissions and incentivesbased on performance and deal closures 

• Continuous  Deployment using GitHub Actions, Flux, Kustomize
• Design and implement cloud solutions, build MLOps on AWS cloud
• Data science model containerization, deployment using Docker, VLLM, Kubernetes
• Communicate with a team of data scientists, data engineers, and architects, and document the processes
• Develop and deploy scalable tools and services for our clients to handle machine learning training and inference.
• Knowledge of ML models and LLM

• 6+ years of experience in ML Ops with strong knowledge in Kubernetes, Python, MongoDB and AWS.
• Good understanding of Apache SOLR.
• Proficient with Linux administration.
• Knowledge of ML models and LLM.
• Ability to understand tools used by data scientists and experience with software development and test automation
• Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS)
• Experience working with cloud computing and database systems
• Experience building custom integrations between cloud-based systems using APIs
• Experience developing and maintaining ML systems built with open-source tools
• Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes
• Experience developing containers and Kubernetes in cloud computing environments
• Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.)
• Ability to translate business needs to technical requirements
• Strong understanding of software testing, benchmarking, and continuous integration
• Exposure to machine learning methodology and best practices
• Good communication skills and ability to work in a team

• Coordinate multiple workstreams simultaneously, ensuring timely delivery and adherence to quality standards.
• Facilitate daily stand-ups and syncs across global time zones, maintaining visibility and accountability.
• Understand business domains and technical architecture to enable informed decisions and proactive risk management.
• Collaborate with data engineers, AI/ML scientists, analysts, and product teams to translate business goals into actionable plans.
• Track project progress using Agile or hybrid methodologies, escalate blockers, and resolve dependencies.
• Own task lifecycle — from planning through execution, delivery, and retrospectives.
• Perform technical reviews of data pipelines, ETL processes, and architecture, identifying quality or design gaps.
• Evaluate and optimize data aggregation logic while ensuring alignment with business semantics.
• Contribute to the design and development of RAG pipelines and workflows involving LLMs.
• Create and maintain Tableau dashboards and reports aligned with business KPIs for stakeholders.
• Mentor and guide team members, fostering knowledge sharing, collaboration, and skill development.

• Strong expertise in data pipelines, architecture, and analytics platforms (e.g., Snowflake, Tableau).
• Experience reviewing and optimizing data transformations, aggregations, and business logic.
• Hands-on familiarity with LLMs and practical RAG implementations.
• Knowledge of AI/ML workflows, model lifecycle management, and experimentation frameworks.
• Proven experience in managing complex, multi-track projects.
• Skilled in project tracking and collaboration tools (Jira, Confluence, or equivalent).
• Excellent communication and coordination skills with technical and non-technical stakeholders.
• Experience working with cross-functional, globally distributed teams.

• Exposure to cloud platforms (AWS) and modern DevOps practices.
• Ability to create executive-level dashboards and summaries for leadership.
• Strong analytical and problem-solving mindset.
• Understanding of semantic layers for consistent business logic interpretation.

• Develop and optimize ETL/ELT pipelines using Python, SQL, and Snowflake to ensure high-quality data for analytics, AI, and LLM workflows.
• Build and manage Snowflake data models and warehouses, focusing on performance, scalability, and security.
• Collaborate with AI/ML teams to prepare datasets for model training, inference, and LLM/RAG-based solutions.
• Automate data workflows, validation, and monitoring for reliable AI/ML execution.
• Support RAG pipelines and LLM data integration, enabling AI-driven insights and knowledge retrieval.
• Partner with business and analytics teams to transform raw data into actionable AI-powered insights.
• Contribute to dashboarding and reporting using Tableau, Power BI, or equivalent tools.

• 5+ years of Data Engineering experience with exposure to AI/ML workflows.
• Advanced expertise in Python programming and SQL.
• Hands-on experience with Snowflake (data warehousing, schema design, performance tuning).
• Experience building scalable ETL/ELT pipelines and integrating structured/unstructured data.
• Familiarity with LLM and RAG workflows, and how data supports these AI applications.
• Experience with reporting/visualization tools (Tableau)
• Strong problem-solving, communication, and cross-functional collaboration skills.

• Experience with MLOps pipelines and ML frameworks (Scikit-learn, TensorFlow, PyTorch).
• Knowledge of cloud data platforms (AWS) and orchestration tools (Airflow, dbt).
• Exposure to containerization (Docker, Kubernetes) and CI/CD practices.
• Ability to partner with business and analytics teams to deliver actionable insights.