Key Responsibilities Execute B2B marketing activities aligned with business objectives and revenue targets Manage the execution of multi-channel campaigns (email, content, digital, webinars, events, etc.) Implement lead-nurturing programs in collaboration with cross-functional teams Support the creation of marketing assets, including case studies, whitepapers, blog posts, landing pages, and email campaigns tailored for B2B audiences Track, analyse, and report on campaign performance metrics Requirements Bachelor’s degree in Marketing, Business, Communications, or a related field; MBA preferred. 2+ years of experience in B2B marketing, preferably within SaaS or enterprise technology. Strong understanding of digital marketing and content strategy. Proficiency with marketing, design, and AI tools (e.g., Canva, Vtiger, Marketo, Salesforce, Google ads, LinkedIn Ads, ChatGPT, etc.). Excellent communication, project management, and analytical skills. Ability to excel in a metrics-driven and collaborative team environment. About Company: Expenzing is the leader in expense management and spending management solutions in our operating markets. We are a pure-play SAAS solution. Show more Show less

Urgently Hiring for AI/ML Role We are seeking an experienced AI/ML Engineer with 3–5 years of hands-on experience in building and deploying machine learning solutions in the fintech and spend management domain. You will work on real-time forecasting, intelligent document processing (invoices/receipts), fraud detection, and other AI-powered features that enhance our finance intelligence platform. This role demands expertise in both time series forecasting and computer vision, as well as a solid understanding of how ML applies to enterprise finance operations. Key Responsibilities: Design, train, and deploy ML models for spend forecasting, budget prediction, expense categorization, and risk scoring. Build and optimize OCR-based invoice and receipt parsing systems using computer vision and NLP techniques. Implement time-series models (Prophet, ARIMA, LSTM, XGBoost, etc.) for forecasting trends in financial transactions, expenses, and vendor payments. Work on intelligent document classification, key-value extraction, and line-item detection from unstructured financial documents (PDFs, scanned images). Collaborate with product and finance teams to define high-impact AI use cases and deliver business-ready solutions. Integrate ML pipelines into production using scalable tools and platforms (Docker, CI/CD, cloud services). Monitor model performance post-deployment, conduct drift analysis, and implement retraining strategies. Required Skills & Qualifications: Core Machine Learning Strong knowledge of supervised and unsupervised ML techniques applied to structured and semi-structured financial data Experience in time-series analysis and forecasting algorithms such as: ARIMA, SARIMA Facebook Prophet XGBoost for regression LSTM / GRU models for sequential data Proficiency in Python and key libraries: scikit-learn, Pandas, NumPy, StatsModels, PyTorch, TensorFlow. Computer Vision & Document AI Hands-on experience with OCR tools such as Tesseract, Google Vision API, or AWS Textract. Knowledge of document layout analysis and field-level extraction using OpenCV, LayoutLM, or Google Document AI. Familiarity with annotation tools (Label Studio, CVAT) and post-processing OCR outputs for structured data extraction. Deployment & Engineering Experience in exposing ML models via Flask or FastAPI. Model packaging and deployment with Docker, version control with Git, and ML lifecycle tools like MLflow or DVC. Working knowledge of cloud platforms (AWS/GCP/Azure) and integrating models with backend microservices. Data & Domain Understanding of financial documents: invoices, receipts, expense reports, and GL data. Ability to work with tabular, image-based, and PDF-based financial datasets. SQL proficiency and familiarity with financial databases or ERP systems is a plus.

About Expenzing: Expenzing is a global leader in spend management solutions, offering a comprehensive cloud-based suite covering strategic sourcing, procurement, and accounts payable. Our products empower enterprises to reduce costs, improve spend governance, and mitigate risk—delivering measurable outcomes for CFOs, finance controllers, procurement heads, and business leaders. Recognized by Gartner, IBM, and NASSCOM, our solutions have been successfully implemented across industries and geographies . Role Overview: As a Business Analyst at Expenzing, you’ll be at the forefront of our client engagements, driving digital transformation initiatives in spend management. You will ensure the successful implementation of Expenzing’s solutions and play a key role in translating business requirements into effective configurations and product enhancements. Key Responsibilities: Lead client requirement discovery and translate needs into detailed configuration specs Oversee and support the implementation of Expenzing’s automation tools Collaborate with development and product teams to guide customizations and enhancements Ensure timely, successful software rollout and client adoption Conduct demos and Proof of Concept sessions for prospective clients Perform business testing for new product features and releases Understand and apply procurement and finance best practices to implementations

About the Role: We’re looking for a proactive and solution-oriented Customer Success Manager to build strong relationships with our clients, ensure smooth onboarding, drive adoption of Expenzing’s solutions, and deliver measurable value. The CSM is the main point of contact post-implementation and plays a key role in retention, satisfaction, and growth of accounts. Key Responsibilities: Be the single point of contact for assigned clients post go-live Build strong, trusted relationships with key stakeholders Drive product adoption and help clients achieve their goals using Expenzing Monitor usage metrics and identify areas of improvement Handle queries, resolve issues, and escalate where needed Collaborate internally with Product, Support, and Implementation teams Conduct periodic reviews with clients and present ROI and usage data Identify upsell/cross-sell opportunities and coordinate with the sales team Ensure contract renewals are smooth and on time

About the role As an Implementation Leader, you will spearhead the successful onboarding and implementation of our enterprise and mid-market clients. You will lead cross-functional teams, manage project delivery, and act as the primary point of contact for clients throughout the implementation lifecycle. This role requires deep domain expertise in expense management or financial software and strong project leadership capabilities. Key Responsibilities Own end-to-end implementation delivery for strategic clients, from kickoff to go-live. Lead client discovery sessions to gather requirements, define workflows, and design solutions. Collaborate with product, engineering, and support teams to tailor the software as per client needs. Create and manage detailed project plans, timelines, and stakeholder communication. Identify and mitigate project risks, ensuring smooth delivery and client satisfaction. Develop repeatable frameworks and best practices for scalable implementation. Coach and mentor junior implementation consultants and project managers. Support pre-sales with effort estimations, solutioning, and technical fit discussions when needed.

Job Summary We are looking for a dynamic AI / ML SAAS Product Development Manager with 10+ years of experience in designing and delivering full-stack enterprise-grade AI led SaaS products. The ideal candidate must have a strong experience in AI / ML. Proficiency in AI Models, LLMs, AI powered chat models with embedding, NLP, fuzzy logic, RAG tools, Python, structured ML, forecasting tools, vector DBs, AI deployment and production tools, is essential. This is to be backed up with a strong foundation in Java , complemented by frontend technologies (ReactJS, React Native) , and strong experience in integration architecture . You should be comfortable leading development teams while remaining hands-on with architecture, code reviews, and feature prototyping . Key Responsibilities Leadership & Strategy ● Pioneer AI/ML research and prototyping ● Lead, mentor, and manage a team of backend, frontend, and mobile developers. ● Create an environment of engineering excellence, innovation, and high accountability. ● Align development efforts with business objectives, delivery timelines, and long-term architecture goals Technical Ownership ● Provide architectural direction and oversee high-level design decisions. ● Review and guide code quality, performance, scalability, and security standards. ● Collaborate with Product Managers and UX Designers to shape the product roadmap. ● Translate business needs into modular, configurable product features. ● Lead initiatives to build SaaS-ready AI architecture with multi-tenancy, usage metering, and integration APIs. ● Work closely with product owners, UX teams, and QA to plan, estimate, and deliver product features. ● Ensure best practices in Agile development, DevOps, and continuous deliveries Hands-On Contribution ● Participate in key coding tasks, AI/ML POCs, and architectural refactoring ● Debug and resolve complex technical issues in backend and frontend layers. Core Technical Skill Sets Backend / Core ● Java (12+ years), Spring, Spring Boot, Hibernate/JPA, RESTful APIs ● Microservices architecture and integration patterns ● SQL (MySQL, PostgreSQL), NoSQL (MongoDB, Redis) ● CI/CD (Jenkins, GitLab CI) ● Cloud: AWS / Azure / GCP Frontend ● HTML5, CSS3, JavaScript, JSP ● jQuery and Bootstrap for legacy and lightweight frontend use ● ReactJS for modern web applications ● REST integration with UI, cross-browser compatibilities Mobile / Cross-Platform ● React Native for hybrid mobile application development AI/ML & Data Engineering ● Familiarity with Python for AI/ML model integration ● Basic understanding of machine learning workflows (scikit-learn, TensorFlow, PyTorch) ● Ability to lead integration of ML models into production environments ● Identify use cases where AI/ML can be embedded for customer value (e.g., recommendations, predictions) ● Prototype and test AI-driven features in production environments with safety and observability.