Cloud / SAAS Sales manager - Channel sales

Review Criteria

• Strong Data Scientist/Machine Learnings/ AI Engineer Profile
• 2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models
• Strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression, decision trees, gradient boosting, etc.
• Hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models
• Strong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text
• Experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments
• Preferred (Company) – Must be from product companies

Job Specific Criteria

• CV Attachment is mandatory
• What's your current company?
• Which use cases you have hands on experience?
• Are you ok for Mumbai location (if candidate is from outside Mumbai)?
• Reason for change (if candidate has been in current company for less than 1 year)?
• Reason for hike (if greater than 25%)?

Role & Responsibilities

• Partner with Product to spot high-leverage ML opportunities tied to business metrics.
• Wrangle large structured and unstructured datasets; build reliable features and data contracts.
• Build and ship models to:
• Enhance customer experiences and personalization
• Boost revenue via pricing/discount optimization
• Power user-to-user discovery and ranking (matchmaking at scale)
• Detect and block fraud/risk in real time
• Score conversion/churn/acceptance propensity for targeted actions
• Collaborate with Engineering to productionize via APIs/CI/CD/Docker on AWS.
• Design and run A/B tests with guardrails.
• Build monitoring for model/data drift and business KPIs

Ideal Candidate

• 2–5 years of DS/ML experience in consumer internet / B2C products, with 7–8 models shipped to production end-to-end.
• Proven, hands-on success in at least two (preferably 3–4) of the following:
• Recommender systems (retrieval + ranking, NDCG/Recall, online lift; bandits a plus)
• Fraud/risk detection (severe class imbalance, PR-AUC)
• Pricing models (elasticity, demand curves, margin vs. win-rate trade-offs, guardrails/simulation)
• Propensity models (payment/churn)
• Programming: strong Python and SQL; solid git, Docker, CI/CD.
• Cloud and data: experience with AWS or GCP; familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau).
• ML breadth: recommender systems, NLP or user profiling, anomaly detection.
• Communication: clear storytelling with data; can align stakeholders and drive decisions.

JOB DESCRIPTION

Senior Product Manager (Technical)

Real-time AI Platform  ·  Enterprise B2B  ·  Multi-Vertical

Company: ableCredit

Location: Bangalore

Reports to: CEO / Co-founder

Team: Engineering, AI/ML, Enterprise Customer Success

Stage: Post-PMF, Pre-Scale

About ableCredit

ableCredit is a GenAI infrastructure company built for enterprises where human conversations drive high-stakes decisions. Our real-time AI co-pilot platform listens to live conversations, guides front-line agents through structured workflows, and captures everything into compliant, structured outputs — automatically.

Why This Role Exists -

This role exists to solve three specific problems:

• Shipping cadence: We build good things but ship unpredictably. We need someone who can install a culture of weekly releases, clear ownership, demo days, and “what shipped this week” accountability.
• AI leverage: We need someone who sees these opportunities instinctively: POC onboarding, SOP configuration, customer telemetry analysis.
• Alignment: The person in this role becomes the connective tissue between Business, engineering, and management - translating customer urgency into engineering priorities without losing either side’s trust.

What You’ll Own

Product Execution & Shipping Culture

• Own the product roadmap for the core ableCredit platform across BFSI, insurance, healthcare, and BPO verticals.
• Install and run a predictable shipping rhythm: sprint planning, release cadence, demo days, weekly “what shipped” updates to the company.
• Own the prioritisation framework: what gets built, what gets deferred, and why. Communicate tradeoffs clearly to engineering, sales, and leadership.

Technical Product Leadership

• Work directly with the engineering and AI teams on architecture decisions, latency tradeoffs, model selection, and evaluation frameworks for AI features.
• Review telemetry dashboards, debug production issues alongside engineers, and read API documentation when integrating with client systems.
• Make platform vs. vertical-specific tradeoffs: know when a feature request genuinely needs custom work and when it’s a configuration problem.

AI as Internal Leverage

• Identify internal workflows that should be automated or agent-powered: POC onboarding, SOP configuration, customer health monitoring, QA of conversation outputs.
• Stay current on AI capabilities (agentic frameworks, evaluation methods, prompt engineering at scale) and translate them into practical product decisions.

Enterprise Customer & Cross-Functional Alignment

• Be the single point of truth between what customers need, what sales has promised, and what engineering is building.
• Join customer calls and on-site visits. Understand the reality of a credit officer in Coimbatore or a branch executive in Nagpur using the product in the field.
• Build a visible, credible roadmap that sales can sell from and customers can plan around .

What We’re Looking For

Non-Negotiables

• 5–8+ years in product management, with at least 2 years shipping AI-powered or data-heavy products to enterprise B2B customers.
• Technical fluency: You’ve written production code at some point in your career. Today, you can read a Grafana dashboard and spot a latency spike, review a PR for product correctness, navigate API docs independently, and check telemetry without filing a ticket. Engineers see you as a technical peer, not a business translator.
• AI product judgment: You understand the difference between AI-as-product and AI-as-infrastructure. You’ve made decisions about model selection, prompt engineering, evaluation frameworks, and graceful degradation in production. Bonus: you’ve built or shipped agentic workflows.
• Shipping culture builder: You’ve joined a team that was building good things but shipping unpredictably, and you changed that. You can describe exactly what you did in the first 90 days.

Strong Preferences

• Multi-vertical or horizontal platform experience: You’ve worked on a product that serves multiple industries and have dealt with “build once, configure many” vs. “this vertical needs something custom” tensions.
• India enterprise context: Comfort with BFSI, regulated industries, or India-specific enterprise sales cycles. You understand that deploying in Tier-2/3 India is a different operating environment than a Valley SaaS playbook.
• Internal AI adoption: Evidence of using AI, automation, or agentic tools to improve your own team’s velocity — not just building AI products for customers.

Nice-to-Haves

• Experience with real-time systems: audio/video streaming, low-latency pipelines, multilingual NLP.
• Startup experience at a comparable stage (post-PMF, pre-scale, 20–80 people).
• Familiarity with Indian language tech, voice AI, or field-force enablement products.

Why Join ableCredit Now

• The AI is real. This isn’t a company that slapped “AI-powered” on a dashboard. Real-time transcription, multilingual voice models, agentic workflows - in production, at scale, in one of the hardest operating environments on earth.
• The expansion is genuinely exciting: four new verticals, a new global product, and 7x revenue growth targeted in a single year.
• You’ll work directly with the founding team, strong engineers, and demanding customers who actually want the product to succeed.

Compensation & Logistics

• Competitive salary + meaningful equity (early-stage, pre-Series A).
• Location:Bangalore, in-office, with occasional travel to customer sites.
• This is a leadership hire. You’ll have a seat at the table on company strategy, not just product decisions.

1. Maximising automation within the organization. 
2. Understand various functions’ systems & automation-related needs and design & implement solutions for them.
3. Manage Learning Management System (LMS) from the technical side including coordination with the LMS provider for white-label LMS development.
4. Provide necessary training to MML employees & students for optimal utilisation of automation & technology solutions. 
5. Add value to the growth of the organization's top line & bottom line.