Product Manager

About the Role: We are looking for a tech-savvy and analytical Product Manager to drive the development of scalable non-trading and investment products. In this role, you will bridge the gap between business goals and engineering execution. You will gain deep exposure to APIs, system workflows, data pipelines, and the complex technical architecture behind a modern fintech platform. If you have a strong background in Stock Broking, CRM platforms, and AI-driven product initiatives — with a problem solving mindset and the ability to manage diverse stakeholders — we want to build the future of trading with you.

Key Responsibilities A. Core Non-Trading & Investment Product Ownership

• Own product features from discovery to launch and post-release optimization
• Understand customer needs, non-trading workflows, and market trends to drive product decisions Convert business requirements into PRDs, user stories, acceptance criteria, and workflows
• Work closely with engineering teams to drive sprint planning, prioritization, and releases
• Analyze product usage and performance to inform roadmap decisions
• Work hands-on with non-trading APIs to enable seamless integrations for internal systems, clients, and third-party platforms
• Lead UAT across web and mobile platforms, ensuring functional correctness and edge-case handling
• Act as a key liaison between business, technology, and leadership to ensure alignment and timely delivery

B. CRM & Salesforce Service Cloud Ownership

• Managed the Salesforce Service Cloud and CRM ecosystem supporting cross-functional teams across the organization.
• Owned and managed the Salesforce Service Cloud CRM ecosystem supporting Partner, Franchisee, Connect (Customer Service), ESOP, TradeOps, Operations, Fund Settlement, Trade Settlement, Compliance, Surveillance, and Risk teams
• Designed and maintained Email-to-Case workflows for automated case creation, routing, and SLA tracking
• Built and governed Customer 360 segmentation models — ETB (Existing to Business), NTB (New to Business), and PTB (Potential to Business) — for personalized servicing
• Led omnichannel servicing strategy integrating voice, email, chat, social, and in-app channels into a unified service layer
• Owned operational reporting and dashboard design for service metrics, SLA adherence, escalation trends, and agent productivity
• Managed external integrations including Locobuzz (social listening & ORM), CSAT systems, and Slash dialer for voice-channel operations
• Coordinated CRM change management, UAT, and release cycles across business and engineering teams

C. WhatsApp Chatbot & Conversational Automation

• Led product definition and delivery for rule-based and AI-powered WhatsApp chatbot experiences.
• Defined business use cases for WhatsApp automation: order tracking, appointment booking, FAQ automation, lead qualification, payment reminders, and customer support routing.
• Designed conversation flows including menu hierarchy, user navigation, escalation logic, button choices, fallback handling, happy paths, and error paths.
• Managed WhatsApp Business Platform compliance: template approval strategy, opt-in flows, session vs. template messaging, and adherence to Meta policies.
• Coordinated backend integrations with CRM, ticketing systems, payment gateways, order management systems, and authentication workflows.
• Tracked and optimized chatbot KPIs: automation rate, containment rate, first response time, CSAT, resolution rate, and human-agent deflection.
• Worked with platforms including Meta, Twilio, Gupshup, and 360dialog for WhatsApp Business API enablement.

D. AI Transformation & Intelligent Automation Initiatives

• Led AI product strategy and delivery across customer experience, operational efficiency, and service governance domains.
• Drove AI transformation initiatives across the CRM and service stack, defining AI product strategy including where AI creates value, cost vs. accuracy tradeoffs, and appropriate AI safety boundaries Delivered AI-powered case summarization to reduce handling time and improve agent context awareness at case resolution
• Built a RAG-based knowledge assistant for internal policy retrieval, product catalogue grounding, and FAQ indexing using Retrieval-Augmented Generation architecture
• Launched AI-assisted draft response capabilities enabling agents to generate, review, and send AI-suggested replies with human-in-the-loop approval workflows
• Enabled chatbot and voice bot integration into service channels, including prompt engineering, system prompt design, persona consistency, guardrail definition, and fallback logic
• Implemented sentiment analysis across customer interactions to detect dissatisfaction signals and trigger proactive escalation
• Deployed compliance alerting and surveillance automation to flag regulatory risks in customer communications in real time
• Built intelligent case classification models to auto-route cases to the correct team, reducing manual triage and improving SLA adherence
• Developed social escalation detection using NLP to identify and priorities high-severity customer complaints originating on social platforms
• Defined AI evaluation frameworks: accuracy benchmarks, hallucination thresholds, safety policies, evaluation datasets, escalation quality metrics, retrieval precision, and groundedness scoring Partnered with engineering on LLM model selection, RAG architecture, cost optimization, and latency targets across OpenAI, Anthropic, and Google AI platforms
• Ensured AI safety and compliance: PII protection, prompt injection prevention, jailbreak handling, and regulatory governance for BFSI-grade AI deployments

E. Analytics, Insights & Continuous Improvement

• Analyzed product usage, service metrics, and AI model performance to continuously refine roadmap priorities
• Tracked end-to-end funnel metrics: completion rates, abandonment, containment, CSAT, resolution rate, token cost, and revenue uplift
• Used data to optimize conversation flow design, message sequencing, button wording, trigger logic, and flow length
• Built and maintained operational reporting dashboards for leadership visibility on AI and CRM program performance

Skills & Qualifications

• 2–5 years of Product Management experience in Stock Broking, Capital Markets, or Fintech
• Strong analytical and problem-solving skills with the ability to translate business needs into clear product requirements
• Functional understanding of APIs, system integrations, and data flows (coding not required) Experience working closely with engineering teams in an Agile environment — sprint planning, prioritization, and release management
• Excellent communication, stakeholder management, and documentation skills

Good to Have (Added Advantage)

• Familiarity with CRM platforms such as Salesforce Service Cloud — configuration, workflows, or cross-team ecosystem management
• Exposure to WhatsApp Business API or conversational chatbot products, rule-based or AI-powered Basic awareness of AI/ML concepts — LLMs, RAG systems, NLP, or prompt engineering — particularly in a fintech or BFSI context
• Experience with AI-driven insights, automation, or decision-support features is a plus, but not required

Why Join Bajaj Broking

• Own high-impact products used by millions of investors
• Gain exposure to large-scale non-trading systems and real-world fintech platforms
• Drive AI transformation across CRM, customer service, and non-trading operations at scale
• Work closely with experienced leaders across product, engineering, and business
• Clear growth path in a rapidly expanding non-trading and investment ecosystem