Customer Relationship Manager

Apply here https://tinyurl [dot] com/3tz4tkht

About Discovered Labs

At Discovered Labs we work with $10M - $50M ARR companies to help them get more leads, users and customers from Google, Bing and AI assistants such as ChatGPT, Claude and Perplexity.

We approach marketing the way engineers approach systems: data in, insights out, feedback loops everywhere. Every decision traces back to measurable outcomes. Every workflow is designed to eliminate manual bottlenecks and compound over time.

High-level overview of our approach:

Data-driven automation: We treat marketing programs like products. We instrument everything, automate the repetitive, and focus human effort on high-leverage problems.

First principles thinking: We don't copy what others do. We understand the underlying mechanics of how search and AI systems work, then build solutions from that foundation.

Full-stack ownership: SEO and AEO rarely work as isolated tasks. We work across the entire funnel and multiple surface areas to ensure we own the outcome and clients win.

The Agency OS

We're building the first agency OS: a system where the agency's work is the software, enabling clients to gain an unfair advantage.

As Karpathy puts it, Software 3.0 automates what humans can verify. Verifiability, not capability, decides which work can safely be handed over. So every eval we write expands the set of work we can trust an agent with, and the harness isn't overhead around the product, it's what makes the product possible. Dex Horthy covers the operational half: the agents that survive contact with customers are well-engineered software with human judgement placed deliberately, and disciplined loops beat vibes.

What that looks like here: expert orchestrators, not operators. One strategist supervising a fleet of agents running real client workflows at scale, with the system surfacing what genuinely needs a human decision and staying quiet about what doesn't. Feedback loops everywhere. Every correction an expert makes becomes an eval case, and the swarm is better the next cycle.

The Team

You'll join the Automations team at Discovered Labs. We build the agent swarm: a growing fleet of agents and workflows that do real client work end to end, and the engineering that makes them trustworthy enough to let loose on it.

Your mandate is both halves of that. Expand what the swarm can do, and prove that what it does is right. That means applying hardcore engineering to a famously slippery problem: making non-deterministic systems reliable, measurable and provable. Evals that fail a build. Traces that explain a decision. Guardrails that hold when a model doesn't. Agents that know what they don't know and say so.

You'll work alongside the Automations lead, and with our AI & Data team who own the platform and tooling underneath you. You won't be building orchestration from scratch or fighting for infrastructure. It's there, and it's good. Your job is to take it the last mile.

Every agent you make provable is an agent we can put in front of a client. That's the whole game, and the ceiling on how fast this company grows.

We're a deeply technical team building the SpaceX of the AEO & SEO space. You'll work alongside engineers who have built fraud engines powering Stripe, shipped AI code review at CodeRabbit, built at Amazon, developed self-driving car systems at Aurora, and conducted AI research at Stanford. We don't have layers of management. You'll work directly with founders who can go deep on architecture, code, and product.

This Role

You'll join the Automations team, and your job is to productionise what we've built.

We have live 24/7 agents, data pipelines and workflow infrastructure. What you’ll be doing is helping us get the stack running robustly, reliably and at scale.

That means evals that catch regressions before they ship, observability that explains why an agent did what it did, provenance that traces every claim back to its source, and interfaces that let a non-engineer SEO strategist run and review the work. You'll build the harnesses and eval suites that gate agent changes in CI, the lineage and step-trace tooling that makes agent reasoning inspectable, and the surfaces the team actually uses. Then you'll close the loop: instrument the output, feed the signal back, and make the agents measurably better every cycle.

You'll also extend what agents can reach. Today they work over APIs and our own data. Next they operate real websites along withsession handling, and navigating interfaces that were never designed for machines.

The hard problem is reliability and legibility. An agent that's right 80% of the time and can't tell you which 80% is worthless. An agent that's right 95% of the time, shows its working, and flags its own uncertainty is a product.

You report to the CTO and work alongside the Automations lead, with our AI & Data team owning the platform beneath you. You own your evals, your CI, and your monitoring.

What You'll Do

Agent eval harnesses. Golden suites, deterministic checks, and online scoring that gate every agent change in CI. A regression should fail a build, not a client report.

Agent observability. Step traces, token and cost accounting, run-level SLOs, and failure taxonomies. You'll know the difference between "the agent ran" and "the agent was right."

Data lineage and provenance. Every claim an agent makes should be traceable to its source. Which inputs, which tool calls, which reasoning steps produced this output.

End-to-end agents with human review. Agents that complete real work start to finish, with a curated review surface so an expert can approve, correct, or reject, and so that correction becomes training signal.

Interfaces for non-engineers. Dashboards and controls that let the SEO team run, inspect and trust agent work without asking an engineer.

Expanding what agents can do. Agents that open pull requests against real repositories, authenticate through OAuth, and operate real sites and CMSs through the browser. Every new capability is a new class of work the swarm can take on.

Closing the loop. Turn signals we already collect, such as AI perception and citation data, into concrete client value: strategy recommendations, prioritised actions, and measurable outcomes.

Algorithms and scoring models. Not everything should be a prompt. You'll build and tune the scoring systems behind what we recommend: internal linking and semantic relevance scoring, Reddit opportunity and thread scoring, content and citation quality. These are ranking and classification problems with real feedback data behind them, and where a model beats a prompt, you build the model.

Shared, composable modules. Reusable capability blocks that both agents and workflows compose, so an improvement lands everywhere at once instead of being reimplemented per agent.

Scaling across clients. Take a workflow that works for one client and make it run reliably for many, with per-client configuration, isolation, and failure that stays contained.

The Ideal Person for This Role

A builder who ships. You care about getting working systems into production, not endless planning or polish. You've built AI systems people actually rely on.

An operator, not just an architect. You don't just design systems, you run them. You find satisfaction in making things reliable, not just making them work once in a demo.

An owner. You take responsibility for outcomes, not just tasks. When an agent silently produces bad output, you catch it, fix it, and build the eval that stops it recurring.

Maniacal about detail. This is the one that matters most here. AI has made it trivially cheap to produce a large volume of plausible-looking work, and most of that work is slop. We are building the opposite: output that holds up when a client reads it line by line. If you'd rather ship one thing that is provably right than ten that are probably fine, this is your team. If you cannon volume and let the reviewer sort it out, it isn't.

Sceptical of your own output. You assume the model is wrong until measured. You've been burned by a demo that worked and a production run that didn't, and you build accordingly.

Humble and curious. You acknowledge what you don't know, ask good questions, and genuinely want to learn. You take feedback as a gift, not a threat.

A first-principles thinker. You understand why things work, not just how. You can go five levels deep on eval design, prompt architecture, and where to put the human in the loop.

Always improving. You're not satisfied with "good enough." You actively seek ways to get better at your craft and make systems better over time.

Requirements

5+ years in software engineering, with meaningful recent time on LLM-backed or ML-backed production systems, including 2+ owning a production system end to end.

Python, React, Typescript and strong systems fundamentals. You write production services, not notebooks.

LLM application engineering in production. Agents, tool use, structured outputs, retrieval, prompt architecture. You've shipped something real that used them and stayed up.

Evaluation systems. You've built eval suites for non-deterministic systems: golden datasets, regression gates, offline and online scoring. You've thought hard about what "correct" means when there are many correct answers.

Observability for AI systems. Tracing, run inspection, cost and token accounting. Langfuse, LangSmith, Braintrust, OpenTelemetry or equivalent.

Debugging non-determinism. You can diagnose why an agent failed on run 47 of 100 and turn that into a permanent check.

Pipeline orchestration. Airflow, Dagster, Temporal or similar. Retries, idempotency, partial failure, resumption.

Third-party API integration. Auth flows, rate limits, pagination, breaking changes. Not just calling endpoints, but handling the full operational reality.

Own your infrastructure. Containers, CI/CD, deployment, monitoring, credential management. No platform team to hand off to.

Product sense for expert users. You've built tooling that domain experts, not engineers, use daily. You know that an unexplained AI output is an unusable one.

Collaborative. You'll define contracts with the engineers who own the infra beneath you. You document decisions, write clear specs, and communicate tradeoffs in writing.

Preferred Qualifications

• Browser automation or authenticated web agents (Playwright, Puppeteer, computer-use models)
• Applied ML: ranking, scoring, classification, or recommendation systems in production
• Python, React, Typescript
• Terraform, Kubernetes
• Prior experience at a fast-moving startup

What's in It for You

• Fully remote position
• Work directly with the CTO on high-impact projects
• High ownership and autonomy. No micromanagement.
• First-hand exposure to cutting-edge AI and search technology
• Your work will directly impact well-known (10M+ ARR) companies' performance
• Join a fast-growing company at the intersection of AI and marketing

Our Hiring Process

1. Application
2. Technical Deep Dive
3. Reference Checks

Apply here https://tinyurl [dot] com/3tz4tkht

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.

About Company

Our client is a leading global co-educational day and boarding school for ages 2-18 having it’s first campus in India based out of Pune. The school will follow the English National Curriculum and IGCSEs with students progressing to the IB Diploma Programme offering unparalleled excellence in sports, music, and the arts.

Roles & responsibilities:

Establish the Human Resource department for the central office as well as the school

• Manages all HR functions including talent acquisition, performance management, employee relations, benefits, and compensation

• Develops and maintains the school’s employee policies and procedures, employee handbook and employee forms

• Manages the recruitment process including posting, recruiting, interviewing, and hiring for open positions

• Supports the Leadership Team in the recruitment of Expat faculty and staff

• Prepares and maintains accurate job descriptions and oversees the employee onboarding, orientation, exit processes and document completion

• Advises and consults with Head of School and relevant leadership team members to resolve and/or diffuse employee issues, oversees and directly engages in the resolution of delicate employee matters and seeks at all times to protect the School’s best interest

• Monitors and submits insurance claims related to worker’s compensation

• Develops, implements and maintains effective compliance with Employment and Labor Laws and Regulations

• Maintains employee records and files

• Oversees VISA, relocation, logistics and administration for Expat faculty and staff 

• Leads Annual Performance Management process for faculty and staff

• Leads professional development initiatives for faculty and staff

• Supervises HR Manager whose key responsibilities will be supporting the HR Team, administering payroll, benefits, relocation and logistics for expats, managing the school’s email account, vacation tracking

• Liaison with International educational authorities for Expat appointments 

• Strong executive coaching skills with a focus on leading through change

• Effective consulting skills covering the full range of HR disciplines

• While the above includes a description of the essential functions of the position, other duties may be assigned.

Requirements:

• Graduate/Postgraduate MBA-HR

• Work experience for an International School

• 15 – 20 years and above of relevant experience

• Excellent verbal and written fluency in English and Hindi.

• Related working experience as Executive Assistant in other international schools or companies is preferred.

• Good communication and coordination skills. 

• Good organisation skills.

• Proficiency in Microsoft 365 (Word, Excel, Outlook, Sharepoint, OneNote, etc)

• Kind, pleasant and effective communicator, willing to serve people at all levels in the organization

• Working knowledge of human resource principles, procedures and practices

• Ability to effectively deal with multiple priorities, functions and activities

• Well-developed business ethics and the ability to maintain a high level of discretion

• Knowledge of Indian, Expat and state employment laws and regulations 

• Knowledge of business and payroll software, including Microsoft Word, Excel and PowerPoint. Knowledge of working on HR ERP would be preferred

• Proficient in the use and application of a range of psychometrics in assessment, selection and development environments

• Excellent verbal and written communication skills with an emphasis on confidentiality, tact and diplomacy

• Knowledge & Experience of Expat appointments 

• Experience of working with an International school will be preferred 

• At least 4 years of experience as a Node.js developer.
• Extensive knowledge of JavaScript, web stacks, libraries, and frameworks.
• Mandatory experience in MongoDB and MySql.
• Knowledge of front-end technologies such as HTML5 and CSS3.
• Superb interpersonal, communication, and collaboration skills.
• Exceptional analytical and problem-solving aptitude.

The District Sales Manager will oversee sales activities within a defined territory and manage sales representatives and distributors.

• Trains sales representatives and distributors in assigned territory.
• Organizes sales schedules of assigned staff.
• To act like a bridge between the company and distributor with providing stock reports and product inventories.
• Conducts performance evaluations that are timely and constructive.
• Handles disciplinary action against employees as needed and in accordance with company policy.
• Mostly to coordinate with Sales executives from his assigned district.
• Sales the company’s products in an assigned territory.
• Monitors existing accounts and regularly communicates with primary contacts for the account.
• Works to establish new accounts in the territory.
• Works toward increasing sales growth in the territory at an established ratedetermined by Company.
• To provide map coordinates and beat plans.
• Performs other related duties as assigned.

• Ability to build and maintain business relationships with clients.
• Excellent verbal and written communication skills.
• Excellent management and leadership skills.
• Excellent time management skills.
• Ability to multitask and complete work while traveling.

• Bachelor’s degree in any domain.
• 3-4 years of core field sales/ marketing experience required.

Location: KEONJHAR

• Document business or systems as-is/ to-be process,
• Assessing the business model or its integration with technology.
• Work closely with Product Owners and
• Business stakeholders in preparing Product Backlog items
• Prepare user stories, prepare acceptance criteria for user stories and manage backlog.
• Drive business workshops to define requirements
• Develop product backlog and elaborate functional specifications for an IT solution.
• The candidate should also be able to analyze requirements
• Model as-is and to be process and support business during testing
• For stakeholders and customers, act as a bridge between the business problems and the technology solutions.