Careers at Sprouts.ai

Join us in building revenue intelligence that helps modern GTM teams discover better opportunities, prioritize the right buyers, and turn data into action.

Why Sprouts

We are a product-led team solving hard data, workflow, and customer growth problems for B2B companies. You will work close to customers, move quickly, and see the impact of what you ship.

Impact at scale

Build products that help revenue teams find the right accounts, understand buyer intent, and act with confidence.

Craft & ownership

Work with focused teams where engineers, designers, and GTM partners own meaningful problems from idea to launch.

Flexibility

Collaborate across locations with a culture built on trust, clarity, and outcomes instead of unnecessary process.

Open roles

We are looking for people who care about useful products, clear communication, and doing high-quality work with a strong sense of ownership.

Posted October 2026

Senior AI Engineer

4+ YearsFull-TimeBangalore / Chandigarh (Hybrid). Remote possible for the right person.

Engineering / AI

Apply now

You will build and ship AI features end to end. You will write production Python, work with LLMs, and own features from design to deployment. You will work closely with the design and product teams.

View full job description

What we are building

We are building an AI-native GTM intelligence platform for enterprise revenue teams. We run large-scale company and contact data, a relationship graph, and an AI search engine on top of it. Our products also include automation of business workflows.

Stack: Python, FastAPI, Postgres, Elasticsearch, Iceberg, and Kubernetes, with LLMs across the product.

What you will actually do

Build AI features using LLMs and multi-agentic workflows.
Write clean, production-grade Python backend services and APIs.
Strong system design and architecture.
Design and improve retrieval and data pipelines that feed the models.
Test and improve the quality of AI outputs.
Debug issues across the stack and keep the system reliable.
Work with the team on architecture and design decisions.

Required skills

  • 4+ years building production software, with recent work on AI or ML systems.
  • Strong Python and solid backend engineering.
  • Hands-on experience with LLMs: prompting, RAG, structured output, tool calling.
  • Comfort reading unfamiliar code and shipping without hand-holding.
  • Clear communication and good engineering judgment.
  • Daily use of AI coding tools.
  • Experience with agent frameworks (LangChain, LangGraph, MCP).
  • Elasticsearch, vector search, or other retrieval systems.

Nice to have

  • Workflow engines (Temporal or similar), Kafka, or Spark.
  • Fine-tuning or self-hosted inference.
  • Experience measuring and improving model quality.

Why this role

  • Real AI product with enterprise customers, not a demo.
  • High ownership and direct impact on what ships.
  • Small, strong team and a fast pace.

Posted June 2026

Product Designer (AI-Native)

6-10 YearsFull-TimeBengaluru (preferred). Remote also considered.

Product / AI Leadership

Apply now

Sprouts.AI is an AI-native Generative Demand Platform redefining the $1.1T B2B Go-To-Market (GTM) category. We help demand-gen and sales teams generate qualified pipeline and close deals faster using real-time account and contact intelligence signals - and agentic AI execution.

We need a Product Designer (AI-Native) who has lived this distinction - someone who has designed products where AI is the core experience, not a feature. You will own the end-to-end design of how our AI agents, intelligence surfaces, and agentic workflows show up to users.

View full job description

What we are building

Our product is not a static SaaS dashboard. It is a living system of data, workflows, connectors, and AI agents - built to fix broken GTM ecosystems driven by dirty data, siloed tools, and static ICPs.

Every screen, every interaction, every data surface is shaped by AI agents working in real-time - not a traditional UI with AI bolted on top.

This is a foundational design leadership role. You will own the design system, define interaction patterns for agent-driven experiences, and shape the product's design philosophy from the ground up.

The kind of engineer we want

  • Believes AI-native products require fundamentally different design thinking than traditional SaaS.
  • Gets excited about designing for AI agents, not just for users clicking buttons.
  • Sees themselves as a design leader who shapes product direction, not just someone who makes things pretty.
  • Prefers ownership over narrow scope - wants to influence the full product experience.
  • Can move fast while maintaining a high-quality bar.
  • Likes building 0→1 design systems and patterns that become the foundation others build on.
  • Is energized by the challenge of making complex AI outputs simple, trustworthy, and actionable.

What you will actually do

AI-Native Product Design

Own the design of Sprouts' core product surfaces: Account Intelligence, Cortex (our AI agent), intelligence dashboards, and agentic workflow interfaces. Design for AI-first interaction patterns: progressive disclosure of AI-generated content, confidence and uncertainty indicators, human-in-the-loop decision flows, and conversational interfaces.

Design System Ownership

Own and evolve the Sprouts Design System - the foundational design language, component library, interaction patterns, and AI-specific design tokens used across all products. Define reusable patterns for agent interactions, signal visualizations, data density layouts, and progressive intelligence disclosure.

Agent and Conversational UX

Design the interaction model for Cortex, our AI agent - how it communicates, recommends, asks for input, surfaces uncertainty, and hands off to human decision-makers. Define the UX for agentic workflows: multi-step AI-driven sequences where the user is a supervisor, not a button-clicker.

User Experience Excellence

Own the end-to-end user experience across personas: Account Executives, Sales Managers, RevOps leaders, and demand-gen teams. Drive UX research and user feedback loops to continuously validate design decisions with real users. Ensure the product is fast, intuitive, and information-dense without being overwhelming.

Cross-Functional Design Leadership

Partner with Product and Engineering to translate requirements into design solutions that are both visionary and shippable. Contribute to product strategy by bringing design thinking to roadmap discussions, feature prioritization, and go-to-market positioning. Mentor and set design standards as the team scales.

Required skills

  • 6-10 years of product design experience with at least 2+ years designing AI-native products (not just AI-enabled features on traditional products).
  • Proven portfolio of shipped AI-native SaaS products where AI is the core user experience.
  • Demonstrated experience designing: agent/conversational UIs, progressive AI content disclosure, confidence/uncertainty patterns, and human-in-the-loop workflows.
  • Design system ownership - built or significantly evolved a design system used across a multi-product platform.
  • Strong systems thinking - designing for complex, interconnected workflows, not isolated screens.
  • Proficiency in Figma with the ability to produce high-fidelity designs and interactive prototypes.
  • Enterprise SaaS experience - understanding of data-dense interfaces, role-based experiences, and constraints of selling to large organizations.
  • Excellent communication - ability to articulate design rationale to engineers, product managers, and founders.
  • Comfort with ambiguity - can operate without a playbook, make design decisions with incomplete information, and iterate fast.

Nice to have

  • Code-based prototyping skills (HTML/CSS/React) - ability to build functional prototypes that go beyond Figma.
  • Experience designing for B2B GTM products: sales intelligence, demand generation, ABM, CRM-adjacent tools.
  • Familiarity with LLM capabilities and limitations - knowing what generative AI can and can't do.
  • Prior startup experience - moving fast, wearing multiple hats, and shipping with limited resources.
  • Experience with Salesforce, HubSpot, or similar CRM ecosystems as a user or designer.
  • Motion design / micro-interaction skills for agent status communication and data transitions.
  • Background in data visualization or information design.

Meta-skills

  • Intelligence-first surfaces: data and AI outputs drive the interface, not the other way around.
  • Progressive disclosure of AI: users see the right level of AI-generated detail at the right time.
  • Trust and transparency: confidence scores, source attribution, and explainability cues are design primitives.
  • Human-in-the-loop by design: AI recommends, surfaces, and drafts - humans review, approve, and course-correct.
  • High data density, low cognitive load: enterprise users need information-rich screens packed without clutter.

Failure, resilience, and chaos thinking

AI outputs are probabilistic, incomplete, and sometimes wrong. You should be comfortable designing for uncertainty: confidence indicators, graceful degradation when AI has low signal, fallback states when agents fail mid-workflow, and transparent error recovery flows. The product must remain trustworthy and actionable even when the underlying AI is uncertain - design is the layer that makes that possible.

How you will know you are succeeding

  • A portfolio showcasing AI-native product design with clear evidence that AI shapes the core UX, not just powers a feature.
  • Specific examples of designing agent interactions, progressive disclosure, trust/transparency patterns, or human-in-the-loop flows.
  • Evidence of design system thinking - patterns, components, and guidelines that scale across a multi-product platform.
  • Clear articulation of your design philosophy for AI-native products and how it differs from traditional product design.
  • Proof that your designs improved real outcomes: user adoption, task completion, time-to-value, or user satisfaction.
  • Demonstration of product thinking - how design decisions connected to business goals and product strategy.

Why this role

  • A foundational design leadership role shaping the UX of an AI-native platform from the ground up.
  • Ownership of the design system, product design philosophy, and all core product surfaces.
  • Direct collaboration with Product and AI leadership and the founders.
  • The challenge of designing for cutting-edge AI agents, intelligence surfaces, and agentic workflows.
  • A fast-moving, high-trust environment where design has a seat at the strategy table.
  • Growth path into leading the Design function as Sprouts scales.

Posted May 2026

Platform Engineer - Infrastructure, Backend & Data

3-10 yearsFull-TimeBangalore / Chandigarh (Hybrid). Remote work is possible for exceptional candidates.

Core Platform - embedded across Infra, Backend, and Data Engineering

Apply now

This is not a classical DevOps role.

It is DevOps reimagined for the AI era, where the boundary between infrastructure, backend, and data pipelines is not a clean handoff but a working surface you operate across every day.

View full job description

What we are building

We are building an AI-native platform designed around LLMs, agents, RAG pipelines, vector stores, queues, and bursty workloads.

Our system runs LLM and inference workloads continuously, handles quiet-then-bursty traffic patterns, has agents calling agents and tools calling tools, will increasingly involve GPUs, and cannot afford runaway infra cost or vendor lock-in.

You do not need to be an AI infrastructure expert on day one. You do need to be curious about it and willing to learn token economics, GPU utilization, and inference patterns as the role demands.

The kind of engineer we want

  • Reads code and does not wait for a Jira ticket with infra requirements before sizing a service or debugging an issue.
  • Writes real Python for internal tooling, deployment automation, and service-level fixes.
  • Thinks in runtime behavior, not diagrams: async patterns, file descriptors, Spark partitioning, retry loops, database pressure, and resource sizing.
  • Treats infra cost as a function of code quality and avoids writing the expensive version in the first place.
  • Is comfortable being wrong, then fixing it with loose opinions and thorough debugging.

What you will actually do

Run cloud infrastructure across multiple providers

Own provisioning, networking, cluster lifecycle, and cost through Terraform and Helm.

Operate Kubernetes seriously

Manage production namespaces, stateless services, async workers, Spark jobs, AI inference workloads, Helm charts, upgrades, and platform stability.

Contribute to the data platform

Work with Spark, Airflow, Iceberg, and Trino on Kubernetes. Size jobs, debug performance, improve DAG infrastructure, and partner with Data Engineering on capacity and reliability.

Sit close to the backend team

Read services, understand connection pooling, async, queues, caching, and Python gotchas. Pair on changes that affect runtime cost and behavior.

Build CI/CD that engineers actually like

Create fast feedback, safe rollouts, easy rollback, and sensible secret management using GitHub Actions and practical pipeline design.

Own observability across the stack

Use Prometheus, Grafana, and Loki for core signals, plus AI-specific metrics like token spend, model latency, and cache hit rates.

Help us think about cost

Use right-sizing, consolidation, spot strategy, and migrations to make meaningful multi-cloud credits last.

Influence design, not just operations

Act as a peer in system design, push back on bad architecture, and ask teams to restructure flows when runtime behavior demands it.

Required skills

  • 3-10 years building production systems as a platform, DevOps, SRE, backend engineer, data engineer, or a mix.
  • Linux, networking, and cloud fundamentals on at least one major provider.
  • Production Kubernetes experience beyond bootstrap tutorials.
  • Comfort with Terraform or equivalent infrastructure as code for real infrastructure.
  • Strong Python experience on backend services, not just scripts.
  • Working knowledge of distributed computing such as Spark, Flink, Dask, Beam, or similar.
  • Experience building or maintaining CI/CD pipelines that real teams depended on.
  • Observability done right: dashboards and alerts that someone other than you actually used.
  • Clear written communication.

Nice to have

  • GPU workloads or AI inference serving in production, such as vLLM, TGI, Triton, SageMaker, or Bedrock.
  • Depth with Iceberg, Trino, Airflow, or Kafka.
  • Cost-attribution work and the ability to explain spend per service or tenant.
  • Backend experience in Rust or Go.
  • Open-source contributions or a homelab we can geek out about.

Meta-skills

  • Reads code and is not afraid of an unfamiliar codebase.
  • Uses AI tools daily, including in this role.
  • Comfortable with ambiguity and fast iteration.
  • Strong engineering intuition.

Failure, resilience, and chaos thinking

AI systems fail in new ways: partial LLM failures, vendor API rate limits, retry storms from agents, streaming interruptions mid-token, and model degradation. You should be comfortable with circuit breakers, graceful degradation, fallback paths, and feature flags at the infra level, and willing to learn AI-specific patterns as we encounter them.

How you will know you are succeeding

  • The platform stays stable through real production load and you are the reason it does.
  • Deployments get faster and rollbacks become boring.
  • A Spark or Airflow problem lands on you and gets fixed without waiting for the Data Engineering queue.
  • A backend bottleneck lands on you and you root-cause it through code, not just metrics.
  • Infra cost per request goes in the right direction and you can explain why.
  • Engineers across infra, backend, and data come to you when they are designing something because they want to, not because they have to.

Why this role

  • You will not be siloed. Most DevOps roles stop at YAML and dashboards; this one puts you across infra, backend, and data with ownership to fix things wherever they break.
  • AI-native means new problems: token economics, GPU utilization, and agent retry storms are genuinely new challenges we are figuring out together.
  • Modern tooling and culture: AI-assisted coding, async-first communication, low ceremony, and high ownership.
  • Hybrid work: about 3 days in office in Bangalore or Chandigarh, 2 from wherever works for you. Full remote can also be discussed.
  • Early enough that decisions matter, stable enough that you will not be firefighting weekends.