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Customer Support Manager

About AbleCredit

AbleCredit has built a foundational AI platform to help BFSI enterprises reduce OPEX by up to 70% by powering workflows for onboarding, claims, credit, and collections. Our GenAI model achieves over 95% accuracy in understanding Indian dialects and excels in financial analysis.

The company was founded in June 2023 by Utkarsh Apoorva (IIT Delhi, built Reshamandi, Guitarstreet, Edulabs); Harshad Saykhedkar (IIT Bombay, ex-AI Lead at Slack); and Ashwini Prabhu (IIML, co-founder of Mythiksha, ex-Product Head at Reshamandi, HandyTrain).

The Opportunity

We are looking for a Customer Support Manager to own day-to-day customer support operations and ensure our customers get fast, reliable resolutions. You will help build a scalable support function owning SLAs, triaging issues, and working closely with Implementation, Product, and Engineering to resolve and prevent customer issues.

What You Will Do?

● Own end-to-end customer support operations across chat, email, and ticketing channels.

● Track SLAs for first response and resolution times across ticket severities.

● Triage and prioritise incoming issues, resolving what you can directly and escalating complex or technical issues to Implementation, Product, or Engineering.

● Build and maintain a knowledge base, FAQs, and troubleshooting guides to reduce repeat tickets.

● Monitor support metrics; CSAT, ticket volume, resolution time, backlog and report trends to leadership.

● Identify recurring issues and root causes, and work with Product and Engineering to drive permanent fixes.

● Personally own escalated or high-severity customer issues, ensuring timely and clear communication until resolution.

● Collaborate with Implementation and Customer Success to ensure smooth handoffs and a consistent customer experience.

● Train and onboard new support team members on product knowledge, process, and tooling.

What You Need To Succeed?

• 3-5 years of experience in customer support, technical support, or support operations, ideally in SaaS or fintech.
• Familiarity with helpdesk or ticketing tools such as Zendesk, Freshdesk, Intercom, or similar.
• Basic understanding of APIs, JSON, integrations, and SaaS product configuration, to triage technical issues effectively.
• Experience defining and tracking SLAs and support metrics such as CSAT, resolution time, and ticket volume.
• Strong problem-solving skills and the ability to stay calm and structured under pressure on escalated or high severity issues.
• Clear, structured written and verbal communication.
• Exposure to BFSI, lending, or financial services customers is a plus.
• Comfortable working in a fast-paced startup environment with evolving processes.

MUST-HAVES:

• LLM, AI, Prompt Engineering LLM Integration & Prompt Engineering
• Context & Knowledge Base Design.
• Context & Knowledge Base Design.
• Experience running LLM evals

NOTICE PERIOD: Immediate – 30 Days

SKILLS: LLM, AI, PROMPT ENGINEERING

NICE TO HAVES:

Data Literacy & Modelling Awareness Familiarity with Databricks, AWS, and ChatGPT Environments

ROLE PROFICIENCY:

Role Scope / Deliverables:

• Scope of Role Serve as the link between business intelligence, data engineering, and AI application teams, ensuring the Large Language Model (LLM) interacts effectively with the modeled dataset.
• Define and curate the context and knowledge base that enables GPT to provide accurate, relevant, and compliant business insights.
• Collaborate with Data Analysts and System SMEs to identify, structure, and tag data elements that feed the LLM environment.
• Design, test, and refine prompt strategies and context frameworks that align GPT outputs with business objectives.
• Conduct evaluation and performance testing (evals) to validate LLM responses for accuracy, completeness, and relevance.
• Partner with IT and governance stakeholders to ensure secure, ethical, and controlled AI behavior within enterprise boundaries.

KEY DELIVERABLES:

• LLM Interaction Design Framework: Documentation of how GPT connects to the modeled dataset, including context injection, prompt templates, and retrieval logic.
• Knowledge Base Configuration: Curated and structured domain knowledge to enable precise and useful GPT responses (e.g., commercial definitions, data context, business rules).
• Evaluation Scripts & Test Results: Defined eval sets, scoring criteria, and output analysis to measure GPT accuracy and quality over time.
• Prompt Library & Usage Guidelines: Standardized prompts and design patterns to ensure consistent business interactions and outcomes.
• AI Performance Dashboard / Reporting: Visualizations or reports summarizing GPT response quality, usage trends, and continuous improvement metrics.
• Governance & Compliance Documentation: Inputs to data security, bias prevention, and responsible AI practices in collaboration with IT and compliance teams.

KEY SKILLS:

Technical & Analytical Skills:

• LLM Integration & Prompt Engineering – Understanding of how GPT models interact with structured and unstructured data to generate business-relevant insights.
• Context & Knowledge Base Design – Skilled in curating, structuring, and managing contextual data to optimize GPT accuracy and reliability.
• Evaluation & Testing Methods – Experience running LLM evals, defining scoring criteria, and assessing model quality across use cases.
• Data Literacy & Modeling Awareness – Familiar with relational and analytical data models to ensure alignment between data structures and AI responses.
• Familiarity with Databricks, AWS, and ChatGPT Environments – Capable of working in cloud-based analytics and AI environments for development, testing, and deployment.
• Scripting & Query Skills (e.g., SQL, Python) – Ability to extract, transform, and validate data for model training and evaluation workflows.
• Business & Collaboration Skills Cross-Functional Collaboration – Works effectively with business, data, and IT teams to align GPT capabilities with business objectives.
• Analytical Thinking & Problem Solving – Evaluates LLM outputs critically, identifies improvement opportunities, and translates findings into actionable refinements.
• Commercial Context Awareness – Understands how sales and marketing intelligence data should be represented and leveraged by GPT.
• Governance & Responsible AI Mindset – Applies enterprise AI standards for data security, privacy, and ethical use.
• Communication & Documentation – Clearly articulates AI logic, context structures, and testing results for both technical and non-technical audiences.