Agentic Universe

Location: Bangalore

Experience: 5–10 years

Type: Full-time | On-site

Start: Immediate

Why this role exists

Most sales teams optimize for activity.

We optimize for outcomes and revenue density.

We are not selling a tool.

We are selling a new way enterprises operate with AI.

This role exists to:

• Win high-value enterprise accounts
• Convert POCs into long-term revenue
• Build accounts that scale from ₹2 Cr → ₹8 Cr ARR

What you’ll do

You will not just manage deals.

You will own revenue creation end-to-end.

1. Close net-new enterprise revenue

• Generate ₹30–50L MRR in net-new bookings by Series A close
• Focus on high-value accounts, not volume deals
• Drive deals from:
• First conversation → signed contract

2. Win Tier-1 insurance accounts

• Target and close at least one of:
• SBI Life Insurance, HDFC Life, ICICI Prudential Life Insurance
• or leading general insurers like
• New India Assurance, Bajaj Allianz General Insurance
• Build relationships with:
• CXOs
• Business heads
• Transformation leaders

3. Run high-conviction POC-driven sales

• Focus on winning strong POCs, not just pilots
• Ensure POCs:
• Are tightly scoped
• Deliver measurable outcomes
• Lead directly to expansion
• Drive clarity on:
• ROI
• Success criteria
• Conversion to long-term contracts

4. Build and expand enterprise accounts

• Create a roadmap to scale accounts from:
• 0 -> ₹2 Cr → ₹8 Cr ARR
• Identify:
• New use cases
• Cross-functional expansion opportunities
• Own:
• Renewals
• Upsell
• Expansion strategy

5. Open new verticals

• Identify and penetrate new industry segments
• Build initial proof points and reference customers
• Work with product and GTM teams to:
• Shape positioning
• Refine messaging

6. Own the full deal cycle

• Drive:
• Discovery
• Solution alignment
• Commercial negotiation
• Closure
• Work closely with:
• Solutions Engineering
• Product
• Leadership

What success looks like

• ₹30–50L MRR closed in new business
• At least one Tier-1 insurer closed
• POCs consistently convert into long-term deals
• Accounts scale into multi-crore ARR relationships
• New verticals show early traction

Who you are

• You have 5–10 years of enterprise sales experience
• You have closed:
• High-value, complex deals
• You are comfortable selling to:
• CXOs
• Senior stakeholders
• You think in:
• Revenue
• Deal strategy
• Long-term account value

What will make you stand out

• Experience selling into:
• BFSI / Insurance
• Track record of:
• Closing large enterprise deals
• Expanding accounts post-sale
• Strong ability to:
• Handle objections
• Navigate complex buying cycles
• Ability to work in:
• High-ambiguity, high-ownership environments

Compensation

₹2,00,000/month (fixed)

(Performance-driven upside aligned with revenue impact)

Why join

• You will work on high-value enterprise deals
• Your work directly impacts:
• Revenue
• Market positioning
• Category creation
• You will build relationships that define long-term growth

What this role is not

• Not a pipeline manager
• Not focused on low-value deals
• Not dependent on heavy support to close

What this role is

• A revenue owner
• A strategic deal closer
• A builder of enterprise relationships

One question to self-evaluate

Can you win a Tier-1 enterprise account, prove value through a POC, and scale it into a multi-crore relationship?

Location: Bangalore

Experience: 2–3 years

Type: Full-time | On-site

Open Roles: 1

Start: Immediate

Why this role exists

Most teams treat QA as the last step.

That’s why bugs reach production.

We are building AI agents where failures are:

• Visible to customers
• Hard to debug
• Expensive to fix

This role exists to ensure:

• Bugs are caught before production
• Releases are safe and predictable
• Teams can ship fast without fear

What you’ll do

You will not just test features.

You will build the system that guarantees quality.

1. Build an automated regression system

• Create a test suite that catches 90%+ of conversation quality issues before release
• Cover:
• Conversation flows
• Edge cases
• Multilingual behavior
• Ensure regression testing is:
• Fast
• Reliable
• Mandatory before deployment

2. Own conversation test harness

• Build a system to simulate:
• Real user conversations
• Edge cases
• Failure scenarios
• Enable teams to:
• Test workflows quickly
• Validate changes before release
• Make daily releases safe and repeatable

3. Reduce production incidents

• Bring P1 incidents down to <1 per account per month, even at 5x scale
• Identify:
• Root causes
• Recurring failure patterns
• Ensure issues are fixed permanently, not patched temporarily

4. Define quality standards

• Establish clear definitions of:
• What is a bug
• What is acceptable quality
• Create:
• QA benchmarks
• Release gates
• Ensure quality is measurable, not subjective

5. Enable fast and safe releases

• Work closely with:
• Engineering
• Product
• Conversation design
• Ensure:
• Testing does not slow down shipping
• Shipping does not compromise quality

6. Move toward zero-bug production mindset

• Build systems where:
• Bugs are rare
• Failures are predictable
• Drive a culture where:
• Quality is owned by everyone
• QA enables, not blocks

What success looks like

• ≥ 90% of bugs caught before production
• P1 incidents drop to <1 per account per month
• Teams can ship daily without fear
• Conversation quality remains consistent at scale
• Production issues become rare and predictable

Who you are

• You have 2-3 years of experience in QA / testing / automation
• You have built:
• Automated test systems
• Regression suites
• You think in:
• Systems
• Failure modes
• You care about:
• Quality
• Reliability
• Real-world performance

What will make you stand out

• Experience testing:
• Conversational systems
• AI / chatbot / voice systems
• Strong understanding of:
• Automation frameworks
• API testing
• End-to-end testing
• Ability to:
• Design test strategies, not just execute them

Why join

• You will define quality standards for an AI platform
• Your work directly impacts:
• Customer experience
• Reliability
• Product trust
• You will move the system from:
• Reactive QA → predictive quality systems

What this role is not

• Not manual testing only
• Not a release blocker
• Not reactive bug reporting

What this role is

• A builder of quality systems
• A guardian of production reliability
• A multiplier of engineering confidence

One question to self-evaluate

Can you build a testing system where bugs are caught before users ever see them?

Location: Bangalore

Experience: 2–5 years

Type: Full-time | On-site

Open Roles: 1

Start: Immediate

Why this role exists

Most engineering teams choose between speed and stability.

We need both.

Today:

• Deployments carry risk
• Cloud costs are higher than they should be
• Compliance is reactive, not built-in

This role exists to build a platform where:

• We can deploy fast without breaking production
• We can scale without runaway cost
• We can pass enterprise InfoSec reviews without firefighting

What you’ll do

You will not just manage infrastructure.

You will build the platform that engineering runs on.

1. Drive cloud cost efficiency

• Reduce Azure compute spend by 40%
• Implement:
• Reserved Instances / savings plans
• Right-sizing of workloads
• Scheduling for non-critical workloads
• Continuously monitor and optimize cost vs performance

2. Build zero-downtime deployment systems

• Ship a deployment pipeline that supports:
• 5+ production deployments per week
• Zero customer-visible downtime
• Implement:
• Blue-green / canary deployments
• Automated health checks
• Safe rollout strategies

3. Enable fast and safe releases

• Reduce time-to-launch significantly
• Ensure:
• High reliability in every release
• Ability to rollback instantly if something breaks
• Create systems where:
• Scaling up is seamless when things go right
• Failures are contained when they don’t

4. Build disaster recovery and compliance readiness

• Create DR/BCP systems that pass enterprise audits from:
• HDFC Life, SBI Life
• Ensure:
• Backup and recovery processes are defined and tested
• Failover strategies are documented and executable
• Build compliance as part of the system, not an afterthought

5. Embed security into the pipeline

• Integrate:
• SAST (Static Application Security Testing)
• DAST (Dynamic Application Security Testing)
• SCA (Software Composition Analysis)
• Secret scanning
• Container scanning
• IaC scanning
• Ensure vulnerabilities are caught before deployment

6. Enforce policy-as-code

• Implement:
• OPA / Gatekeeper
• Azure Policy
• Prevent non-compliant infrastructure from being deployed
• Ensure consistency across environments

7. Build a scalable platform layer

• Create systems that:
• Support increasing deployment frequency
• Maintain reliability under scale
• Work closely with backend and SRE teams to:
• Improve system stability
• Reduce operational overhead

What success looks like

• Cloud costs reduce by ≥ 40%
• Deployments are:
• Frequent
• Safe
• Invisible to customers
• Rollbacks are instant and reliable
• DR/BCP passes enterprise audits in the first attempt
• Security is embedded in the pipeline, not patched later
• Engineering teams ship faster with confidence

Who you are

• You have 2-5 years of experience in DevOps / Platform Engineering
• You have worked with:
• Cloud platforms (Azure preferred)
• CI/CD systems
• Infrastructure as Code
• You think in:
• Systems
• Trade-offs (speed vs reliability vs cost)
• You are comfortable owning:
• Production infrastructure
• Deployment systems

What will make you stand out

• Experience with:
• High-frequency deployment systems
• Cost optimization at scale
• Security-first pipelines
• Strong understanding of:
• Kubernetes / container orchestration
• Monitoring and observability
• Distributed system reliability
• Experience passing enterprise security/compliance audits

Why join

• You will define how engineering ships and scales
• Your work directly impacts:
• Reliability
• Cost
• Deployment velocity
• You will build a platform that moves from:
• Fragile → predictable and scalable

What this role is not

• Not manual infra management
• Not reactive firefighting
• Not limited to CI/CD maintenance

What this role is

• A builder of deployment systems
• A driver of cost efficiency
• A guardian of reliability and compliance

One question to self-evaluate

Can you build a platform where we deploy faster, spend less, and never break production?

Location: Bangalore

Experience: 2–5 years

Type: Full-time | On-site

Open Roles: 2

Start: Immediate

Why this role exists

Most systems work at a low scale.

Very few survive real production load, complex workflows, and enterprise edge cases.

We are building a platform that must:

• Scale from 500K → 20M+ interactions/month
• Handle complex insurance workflows reliably
• Become easier to deploy as it grows, not harder

This role exists to build the backend foundation that makes this possible.

What you’ll do

You will not just write services.

You will design and own core platform systems.

1. Scale the platform without breaking architecture

• Scale from 50K → 2M+ interactions/month
• Ensure:
• High availability
• Low latency
• Fault tolerance
• Avoid large rewrites — build systems that evolve cleanly

2. Build the workflow automation (WA) engine

• Design a flexible system with:
• States
• Stages
• Cohorts
• Dynamic workflows
• Ensure workflows:
• Handle edge cases reliably
• Can be configured easily
• Move from:
• Hardcoded flows → configurable execution engine

3. Build the insurance-specific data layer

• Design data models for:
• Policy states
• Claim workflows
• Consent tracking
• Ensure the system works across:
• Multiple insurers
• Multiple use cases
• Build a platform-first data layer, not use-case-specific hacks

4. Make deployment and setup simple

• Ensure workflows and data models are:
• Easy to configure
• Easy to launch
• Reduce friction for:
• Product teams
• Deployment teams

5. Create a compounding data advantage

• Build a data layer that:
• Improves with every deployment
• Captures structured signals
• Ensure data becomes a long-term edge, not just storage

6. Own production reliability

• Participate in on-call rotation across 3 engineers
• Ensure:
• Incidents are handled quickly
• Root causes are fixed permanently
• Build systems where reliability is shared, not individual

What success looks like

• Platform scales to 2M+ interactions/month smoothly
• Workflow engine supports complex, dynamic use cases
• Data layer enables fast deployment across accounts
• Edge cases are handled without constant firefighting
• System becomes easier to use as it grows
• Production issues are rare and predictable

Who you are

• You have 2-5 years of backend engineering experience
• You have built:
• Scalable systems
• Distributed services
• You think in:
• Systems
• Data models
• Trade-offs
• You are comfortable owning:
• Architecture
• Production systems

What will make you stand out

• Experience building:
• Workflow engines
• State machines
• Data-heavy platforms
• Strong understanding of:
• System design
• Distributed systems
• Failure handling
• Experience working in:
• High-scale production environments

Why join

• You will build the core backend of an AI platform
• Your work directly impacts:
• Scale
• Reliability
• Product capability
• You will design systems that move from:
• Use-case specific → platform-level infrastructure

What this role is not

• Not just API development
• Not limited to feature-level work
• Not disconnected from production realities

What this role is

• A system architect
• A builder of scalable platforms
• A driver of long-term technical advantage

One question to self-evaluate

Can you design backend systems that scale, handle edge cases, and become easier to use as they grow?

Location: Bangalore

Experience: Fresher-2 years

Type: Full-time | On-site

Open Roles: 2

Start: Immediate

Why this role exists

Most AI agents fail not because of models—

but because they sound wrong, behave inconsistently, and don’t handle real conversations.

This role exists to own the craft layer:

• Make agents sound natural, human, and trustworthy
• Make them reliable across real-world conversations
• Make them fast to deploy without endless iteration cycles

What you’ll do

You will not just write prompts.

You will design conversational systems that perform in production.

1. Own conversational quality across deployments

• Achieve 80%+ quality scores on insurer QA audits
• Design conversations across:
• Hindi
• English
• 3+ regional languages
• Ensure agents sound:
• Natural
• Context-aware
• Consistent across interactions

2. Build a reusable prompt and workflow library

• Reduce new workflow launch time from 3 weeks → 5 days
• Create standardized:
• Prompt templates
• Conversation flows
• Objection handling patterns
• Ensure new deployments do not start from scratch

3. Design human-like conversational behavior

• Move agents from:
• Scripted → adaptive
• Robotic → human-like
• Focus on:
• Tone
• Pauses
• Context retention
• Personalization
• The output should feel like:
• “This sounds human”
• not
• “This sounds like a bot”

4. Improve agent performance continuously

• Analyze:
• Call transcripts
• Failure cases
• Drop-offs
• Iterate prompts and flows based on real usage data
• Ensure performance improves across:
• Conversion
• Resolution
• Customer experience

5. Reduce deployment friction

• Minimize back-and-forth during agent setup
• Create clarity in:
• Requirements
• Conversation logic
• Expected outcomes
• Enable faster, cleaner deployments with fewer iterations

6. Align conversations with business outcomes

• Design conversations that drive:
• Conversions
• Retention
• Resolution
• Ensure prompts are not just “good”—they are effective

What success looks like

• QA scores consistently ≥ 80% across languages
• New workflows go live in ≤ 5 days
• Agents sound natural and human-like
• Iteration cycles reduce significantly
• Conversation quality directly improves business outcomes

Who you are

• You have 0-2 years of experience in conversation design, content, or AI prompting
• You have worked on:
• Chatbots / voice bots / conversational systems
• You understand:
• Language nuances
• User psychology
• Real-world conversation flows
• You care about:
• Detail
• Quality
• Real user experience

What will make you stand out

• Experience working with:
• Multilingual conversational systems
• Strong writing and structuring ability
• Familiarity with:
• LLM prompting techniques
• Conversation analytics
• Ability to debug:
• Why a conversation failed, not just rewrite it

Compensation

Upto ₹50,000/month (fixed)

(Aligned with role scope and impact expectations)

Why join

• You will define how AI agents actually feel to customers
• Your work directly impacts:
• Customer experience
• Conversion rates
• Product perception
• You will build the craft layer of an AI platform

What this role is not

• Not generic content writing
• Not static script creation
• Not disconnected from real user interactions

What this role is

• A designer of conversational systems
• A builder of human-like AI experiences
• A driver of quality and performance in AI agents

One question to self-evaluate

Can you design conversations that consistently feel human, handle edge cases, and drive outcomes at scale?

Location: Bangalore

Experience: 3-5 years

Type: Full-time | On-site

Start: Immediate

Why this role exists

Most companies are using LLMs.

Very few are building an advantage from them.

Right now, LLM cost is our largest margin constraint, and model behavior is still too generic to be defensible.

This role exists to:

• Turn LLM usage into a cost-efficient system
• Build compounding intelligence across accounts
• Create a differentiated analysis layer that competitors can’t replicate

What you’ll do

You will not just build models.

You will own the intelligence and cost structure of the platform.

1. Drive down LLM cost dramatically

• Reduce cost per interaction from ₹40 → ₹2 within 6 months
• Implement:
• Model tiering (right model for the right task)
• Caching strategies (semantic + response caching)
• Batching and async processing
• PTU / reserved capacity optimization
• Ensure performance does not degrade while reducing cost

2. Optimize infrastructure spend

• Reduce cloud spend from ₹20L/month → ₹4L/month
• Work across infrastructure layers (Azure / compute / inference)
• Balance:
• Latency
• Cost
• Throughput
• Treat infra as a first-class optimization problem

3. Build the fine-tuning and learning pipeline

• Design systems where:
• Every interaction improves future performance
• Build pipelines for:
• Fine-tuning
• Feedback loops
• Continuous model improvement
• Ensure the 5th customer deployment is structurally better than the 1st

4. Create a differentiated intelligence layer

• Build analysis systems that:
• Extract signals from interactions
• Improve decision-making
• Drive outcome improvements
• Move beyond responses → insight + action

5. Enable new AI-native product categories

• Identify opportunities where:
• AI enables workflows that were not previously possible
• Build foundational ML capabilities to unlock those categories
• Focus on creation, not just efficiency

6. Commoditize LLM usage internally

• Abstract complexity of LLM usage from product teams
• Build internal systems where:
• Cost is predictable
• Performance is consistent
• Make LLM usage a reliable utility layer

What success looks like

• Cost per interaction drops to ₹2 or lower
• Infrastructure spend reduces 5x without performance loss
• Model performance improves with every deployment
• Platform develops a clear intelligence advantage
• New AI-native capabilities become possible due to your systems

Who you are

• You have 3-5 years of experience in ML / applied AI / systems engineering
• You have worked with:
• LLMs
• Inference optimization
• Production ML systems
• You think in:
• Systems
• Trade-offs (cost vs latency vs quality)
• You care about real-world impact, not just model metrics

What will make you stand out

• Experience with:
• LLM optimization (prompting, fine-tuning, distillation)
• Distributed systems or infra-level optimizations
• High-scale inference systems
• Built systems that:
• Reduced cost significantly
• Improved performance over time
• Strong understanding of:
• Caching strategies
• Model routing
• Evaluation frameworks

Why join

• You will directly impact company margins and scalability
• Your work defines whether we have a defensible ML advantage
• You will build systems that move from:
• Generic AI usage → compounding intelligence

What this role is not

• Not research-only
• Not experimentation without production impact
• Not isolated from product and business outcomes

What this role is

• A builder of ML systems at scale
• A driver of cost and performance optimization
• A creator of long-term competitive advantage

One question to self-evaluate

Can you build ML systems that get cheaper, smarter, and more valuable with every interaction?

Location: Bangalore

Experience: 2–5 years

Type: Full-time | On-site

Start: Immediate

Why this role exists

Most systems don’t fail because of one big outage.

They fail because reliability is treated as an afterthought.

Right now, uptime depends too much on individual heroics.

That doesn’t scale.

This role exists to build a reliability system where:

• Uptime is predictable
• Failures are contained
• Escalations don’t depend on leadership

What you’ll do

You will not just monitor systems.

You will own reliability as a product.

1. Drive uptime to production-grade reliability

• Improve system uptime to 99.9% customer-facing SLA within 4 months
• Define and track:
• SLAs / SLOs / error budgets
• Ensure reliability is measured from the customer’s perspective, not internal metrics

2. Build incident response as a system

• Set up a 24/7 incident response rotation across 3 engineers
• Eliminate dependency on leadership (no single escalation point)
• Define:
• Incident severity levels
• Response playbooks
• Escalation protocols
• Ensure fast detection → containment → resolution

3. Contain and fix erratic system behavior

• Identify and resolve:
• Latency spikes
• Downtime incidents
• Integration failures
• Build guardrails to prevent recurrence
• Focus on root cause elimination, not temporary fixes

4. Create continuous reliability feedback loops

• Work closely with engineering teams to:
• Surface recurring failure patterns
• Improve build quality
• Reduce production bugs
• Ensure learnings from incidents directly improve future releases

5. Improve observability and monitoring

• Build dashboards and alerts for:
• System health
• Performance metrics
• Failure signals
• Ensure issues are detected before customers report them

6. Reduce operational fragility

• Remove single points of failure (people, systems, workflows)
• Improve system resilience across:
• Deployments
• Integrations
• Runtime environments

What success looks like

• Uptime reaches 99.9%+ reliably
• Incidents are:
• Detected early
• Contained quickly
• Resolved permanently
• No dependency on a single individual for escalation
• System behavior becomes predictable and stable
• Engineering teams ship with higher reliability confidence

Who you are

• You have 2-5 years of experience in SRE / DevOps / backend systems
• You have worked on production systems with real uptime expectations
• You think in:
• Systems
• Failure modes
• Trade-offs
• You are comfortable debugging live, high-pressure environments

What will make you stand out

• Experience with:
• Distributed systems
• Cloud infrastructure (AWS / Azure / GCP)
• Monitoring & alerting tools
• Have built or improved:
• Incident response systems
• Reliability frameworks
• Strong debugging skills across:
• Infra
• Application
• Integrations

Compensation

₹60,000/month (fixed)

(Aligned with role scope and impact expectations)

Why join

• You will define reliability standards for a production AI platform
• Your work directly impacts:
• Customer trust
• Product performance
• Enterprise readiness
• You will move the system from reactive → predictable

What this role is not

• Not just monitoring dashboards
• Not limited to handling tickets
• Not dependent on escalation to leadership

What this role is

• A builder of reliability systems
• A guardian of uptime and performance
• A multiplier of engineering quality

One question to self-evaluate

Can you build a system where downtime is rare, predictable, and never dependent on a single person?

Location: Bangalore

Experience: 3–5 years

Type: Full-time | On-site

Start: Immediate

Why this role exists

Most AI products fail not because they lack capability, but because they don’t deliver repeatable outcomes at scale.

We are building a platform where AI agents don’t just respond — they execute workflows reliably across enterprises.

This role exists to ensure:

• We can deploy across multiple Tier-1 customers without breaking
• We can measure outcomes, not activity
• We become impossible to replace at renewal

What you’ll do

You will not manage a backlog.

You will own the product as a system that drives business outcomes.

1. Build the workflow execution platform (v2)

• Ship a platform that supports 3+ concurrent Tier-1 deployments without custom forks
• Eliminate one-off implementations — move toward configurable, reusable systems
• Ensure the product works across real enterprise environments, not ideal ones

2. Own outcome tracking and monetization layer

• Design instrumentation to track:
• Business outcomes (conversion, recovery, resolution)
• Not just interactions or usage
• Enable hybrid pricing models (Stage 2) across multiple accounts
• Ensure every deployment can prove ROI clearly

3. Convert customer reality into product roadmap

• Work directly with insurers like:
• SBI Life Insurance, HDFC Life, ICICI Prudential Life Insurance
• Translate:
• Feedback
• Failure modes
• Operational friction
• into clear product priorities
• Build features that make the platform 2x harder to rip out at renewal

4. Create new AI-native product segments

• Identify workflows that were not possible before AI
• Build entirely new categories of automation and execution
• Go beyond efficiency — enable new revenue or capability layers

5. Drive cost reduction and operational efficiency

• Reduce dependency on:
• Support teams
• Manual intervention
• Move toward self-serve, reliable execution
• Target: support dependency → near zero

6. Improve activation and time-to-value

• Reduce deployment/activation time to 7–21 days
• Work closely with deployment teams to remove friction
• Ensure customers see value within weeks, not months

7. Own customer experience of the product

• Ensure live customers:
• Are satisfied
• See consistent performance
• Track and improve:
• Reliability
• Latency
• Outcome success rates

What success looks like

• Platform supports multiple Tier-1 deployments without breaking
• Outcome tracking is clear, trusted, and monetizable
• Customers can prove ROI within weeks
• Product becomes deeply embedded and hard to replace
• Activation happens in ≤ 21 days
• Support dependency drops significantly

Who you are

• You have 3-5 years of product management experience
• You have built systems, not just features
• You are comfortable working in:
• Ambiguous environments
• Customer-facing contexts
• You think in:
• Outcomes
• Trade-offs
• Systems design

What will make you stand out

• Experience building enterprise SaaS or AI platforms
• Strong understanding of:
• Workflow systems
• Integrations
• Operational constraints
• Have worked closely with customers to shape product direction
• Have built products that are used daily in production environments

Why join

• You will define how AI actually works in enterprises
• Your work directly impacts:
• Revenue
• Retention
• Product defensibility
• You will build a platform that moves from demos → real execution

What this role is not

• Not a backlog manager
• Not a feature prioritizer in isolation
• Not disconnected from customers

What this role is

• A system builder
• A translator of customer reality into product truth
• A driver of outcome-based product thinking

One question to self-evaluate

Can you build a product that works across multiple enterprise customers without breaking — and proves its value every single day?

Location: Bangalore

Experience: 2-5 years

Type: Full-time | On-site

Start: Immediate

Why this role exists

Most companies think HR is about policies.

It isn’t.

It’s about building a system where great people join, stay, perform, and scale.

We are at a stage where hiring, performance, and culture cannot depend on founders anymore.

This role exists to build a repeatable, investor-grade people engine.

What you’ll do

You will not “manage HR.”

You will design and run the operating system for people.

1. Build a high-performance hiring engine

• Own hiring across functions (engineering, GTM, ops)
• Design structured hiring processes (scorecards, interview loops, decision frameworks)
• Ensure quality of hire improves over time, not just speed
• Build pipelines for leadership hiring (not just execution roles)

2. Design compensation & ownership frameworks

• Build clear, defensible compensation bands
• Create an ESOP framework aligned with long-term value creation
• Ensure all comp decisions can withstand Series A investor scrutiny

3. Create a world-class onboarding system

• Design onboarding that drives productivity in weeks, not months
• Standardize onboarding playbooks across roles
• Track onboarding success through measurable outcomes

4. Own performance management

• Design and run performance review cycles
• Create clarity on:
• What good looks like
• How performance is measured
• How decisions are made (promotions, exits, rewards)
• Enable managers to run high-quality performance conversations

5. Build team health & operating rhythm

• Own:
• Leave management
• Team well-being
• Feedback loops
• Identify early signals of:
• Burnout
• Misalignment
• Performance drift
• Intervene before problems become visible

6. Develop leadership pipeline

• Identify and hire future leaders
• Work with founders to define leadership expectations
• Build succession depth across teams

7. Create an intern-to-full-time talent engine

• Design structured intern programs
• Train interns into deployable talent
• Build a bench of pre-trained candidates ready for rapid hiring

What success looks like

• Hiring becomes predictable, high-quality, and fast
• New hires become productive within weeks
• Compensation and ESOP structures are clear and defensible
• Performance reviews are objective and trusted
• Team health issues are caught early and resolved
• There is always a ready talent pipeline (interns + leadership)

Who you are

• You have 2-5 years of experience in people operations / talent
• You have built or scaled hiring and people systems, not just executed processes
• You think in systems and frameworks, not policies
• You are comfortable working closely with founders and influencing decisions
• You care deeply about performance, not just culture optics

What will make you stand out

• Experience in high-growth startups (Series A/B stage)
• Designed comp/ESOP frameworks from scratch
• Built structured hiring and performance systems
• Worked on leadership hiring and org design
• Can combine data + judgment in people decisions

Why join

• You will build the people foundation of a scaling company
• Your work directly impacts team quality, speed, and culture
• You will operate close to leadership and influence core decisions

What this role is not

• Not an admin or policy management role
• Not limited to payroll, compliance, or coordination
• Not reactive HR support

What this role is

• A builder of systems
• A guardian of talent quality
• A multiplier of team performance

Location: Bangalore

Experience: 2-5 years

Type: Full-time | On-site

Start: Immediate

Why this role exists

Enterprise deals are not won by decks.

They are won when a customer sees their exact problem solved in front of them.

Today, that burden sits too heavily on leadership.

This role exists to build a repeatable, high-conversion demo and solutioning engine — one that can independently convert top-tier enterprise conversations into signed POCs.

What you’ll do

You will not “support sales.”

You will own the technical win.

1. Convert enterprise demos into signed POCs

• Own pre-sales for Tier-1 accounts including:
• SBI Life Insurance, HDFC Life, ICICI Prudential Life Insurance
• New India Assurance, HDFC ERGO General Insurance, Bajaj Allianz General Insurance
• Convert 5+ Tier-1 demos into signed POCs within 6 months
• Drive technical confidence to unblock decision-making

2. Run high-conviction demo motions

• Lead structured, high-stakes demos where:
• Use case clarity is sharp (not generic AI talk)
• Solution mapping is precise
• Customer objections are addressed in real-time
• Move from “interesting conversation” → “clear next step”

3. Own solution architecture in pre-sales

• Translate business problems into:
• Clear solution architecture
• Deployment feasibility
• Integration approach
• Work across systems like:
• CRM (e.g. Salesforce)
• Telephony (e.g. Exotel)
• Policy/admin systems

4. Eliminate founder dependency in pre-sales

• Take ownership of technical conversations currently handled by leadership
• Become the default authority in:
• Solutioning
• Technical validation
• Pre-POC architecture

5. Build a repeatable demo + proposal system

• Standardize:
• Demo flows by use case (persistency, renewals, claims, sales)
• Proposal structures
• Technical discovery frameworks
• Ensure conversion improves with every iteration

What success looks like

• 5+ Tier-1 POCs closed within 6 months
• Demo → POC conversion becomes predictable
• CTO is no longer required in most pre-sales calls
• Customers leave demos with clarity, not curiosity

Who you are

• You have 2-5 years of experience in solutions engineering / pre-sales
• You have worked on enterprise deals with complex stakeholders
• You can translate business problems into technical solutions quickly
• You are comfortable being the most accountable person in the room
• You don’t rely on slides — you rely on clarity and conviction

What will make you stand out

• Experience selling into BFSI / insurance enterprises
• Strong understanding of:
• Customer workflows (sales, servicing, renewals)
• Enterprise integrations
• Have personally contributed to closing large deals
• Can handle CXO-level conversations without escalation

(Aligned with ownership and impact expectations)

Why join

• You will work on high-stakes enterprise deals from Day 1
• Your work directly impacts revenue and category creation
• You will help define the pre-sales engine of an AI company

What this role is not

• Not a demo operator
• Not a slide presenter
• Not dependent on leadership to close deals

What this role is

• A technical closer
• A problem translator
• A system builder for enterprise conversion