Technical AI Product Manager (Agentic AI)

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The Mandate: Lead the Generative AI revolution at Habuild.

Habuild is the world’s largest online yoga community, reaching over 1.8 Crore users. We are fully bootstrapped, profitable, and growing at a triple-digit YoY rate. We are looking for an elite Product Manager (GenAI) to join our core team in Bangalore and take full ownership of our AI-driven wellness initiatives.

About the Role: You will build the intelligence layer that guides millions through their daily health journey. This is a Zero-to-One ownership role at the intersection of consumer psychology, conversational AI, and wellness. You won’t just be managing a roadmap; you will be architecting the conversational solutions that make our platform intuitive, empathetic, and impactful for a non-tech-savvy audience (45+).

Key Responsibilities:

• Own the GenAI Vision: Identify high-impact conversational use cases (e.g., personalized coaching, subscription handling, habit-reinforcement) and define the roadmap from ideation to public launch.
• Technical Architecture: Collaborate with engineering to select and integrate the GenAI stack, including LLMs (OpenAI, Anthropic, Gemini), RAG pipelines, and vector databases, ensuring a balance between performance, latency, and cost.
• Prompt Engineering & Data: Work hands-on with chat logs and documentation to perform iterative prompt engineering and build retrieval pipelines that ensure contextual accuracy.
• Design Trustworthy Flows: Craft on-brand conversational experiences, including fallback logic and escalation triggers, ensuring our AI feels human and supportive.
• Data-Driven Iteration: Define KPIs (resolution rate, accuracy, user satisfaction), interpret complex data, and run high-velocity experiments to optimize the user experience.

Skills & Experience:

• Experience: 4-10 years of total product management experience, with at least 1 year building Generative AI or NLP-based products (chatbots, virtual assistants).
• Technical Fluency: Familiarity with LLM APIs, vector databases, and RAG frameworks. You are comfortable discussing technical logic (Python, prompt engineering) with our engineering team.
• User-Centered Design: Strong empathy for the user; ability to craft intuitive experiences for people who aren't "tech-savvy."
• Quality Ownership: A high bias for shipping polished, reliable solutions. You have an eye for edge cases, hallucinations, and unintended behaviors.
• Communication: A clear, concise communicator who bridges the gap between engineering, customer care, and leadership.

The "Habuild" Experience: To truly align with our mission, we ask that you experience our platform firsthand. During the process, we encourage you to join our free yoga program via habit.yoga and share your feedback on how we can improve.

Why Join Habuild?

• Real Impact: Your AI products will directly improve the well-being of millions who rely on us every morning.
• True Ownership: You define the vision, the stack, and the roadmap. No VC red tape—just pure execution.
• Compensation: We offer a top-tier cash salary and substantial ESOPs.
• Mission-Driven: Join a profitable, fast-scaling movement where your work is visible and meaningful from Day 1.

Location: In-office (Bangalore)

-Turn ambiguous ideas into clear product requirements and workflows.

- Break large ideas into small experiments that can be shipped quickly.

- Communicate customer problems, edge cases, and product intent clearly to engineers and designers.

- Work closely with engineering throughout implementation rather than simply handing over a PRD.

- Test the product yourself and continuously improve it based on actual usage.

### Customers & Market

- Speak directly with customers and prospects.

- Understand how they currently solve problems and where existing tools fail.

- Run product demos and gather structured feedback.

- Separate individual customer requests from broader product opportunities.

- Use customer conversations to continuously refine product strategy.

### Launch & Growth - Own launch strategy for the products and features you build.

- Define positioning, messaging, demos, onboarding, and initial adoption strategy.

- Work across product, engineering, sales, and customers to make launches successful.

- Measure what happened after launch and iterate quickly.

## What We're Looking For

-You likely have **2–4 years of experience** across product, engineering, startups, consulting, AI, or related roles. More importantly, you should have: - **~1 year of hands-on experience building with LLMs, agents, or AI applications.**

- Built actual AI workflows rather than only used ChatGPT or managed an AI project.

- Strong product judgment and ability to simplify complicated problems. - Comfort working with engineers and understanding technical trade-offs.

- Ability to perform deep market and customer research independently. - Strong written and verbal communication.

- High ownership — you naturally move from identifying a problem to getting something shipped.

- Comfort operating in an environment where the answer is often not known beforehand.

## Two Traits We Care About Deeply

### Constraint Thinking Great product people don't start with: *"What could we build?"* They start with: *"Given our users, technology, time, team, data, and constraints, what is the smartest thing we can build?"*

We value people who can reduce a large possibility space into a small number of high-leverage decisions. You should be able to distinguish between: -

interesting vs. useful,

- possible vs. reliable, AI Product Manager 2 / 4 AI Product Manager - demoable vs. production-ready,

- customer requests vs. underlying problems,

- AI capability vs. AI product.

### Grounded Energy AI moves incredibly fast, and it is easy to either become overly skeptical or overly excited. We want someone who has **high energy without hype**. You should be excited enough to constantly experiment with new technology, while grounded enough to recognize its limitations. You should naturally ask:

- Does this actually work?

- How often does it fail?

- What happens at scale?

- Can we evaluate it?

- Will customers actually change their behaviour for this?

- Is AI even necessary here?

## You Might Be a Great Fit If You have independently done things like: - Built an agent over a weekend to test a product idea.

- Compared multiple LLMs for a real use case.

- Created an eval dataset to test AI quality.

- Used tools such as Claude Code, Codex, Cursor, LangGraph, OpenAI Agents SDK, MCP, Langfuse, or similar systems.

- Automated parts of your own product or research workflow with agents.

- Interviewed users and changed the product based on what you learned.

- Gone from idea to prototype to customer feedback to production. - Read product documentation, GitHub repos, Reddit discussions, research papers, and competitor products to understand a market deeply.

## This Role Is Probably Not For You If

- You primarily see PM as backlog management and sprint coordination. - You need detailed requirements before you can start working.

- You prefer delegating prototyping and experimentation entirely to engineering.

- You haven't spent meaningful time building with modern AI tools.

- You enjoy creating large strategy documents more than testing ideas with users.

- You are uncomfortable making decisions with incomplete information.

## What Success Looks Like Within your first few months, you should be able to independently: AI Product Manager 3 / 4 AI Product Manager - understand a customer problem,

- research the market,

- identify a promising product opportunity,

- prototype an AI workflow,

- define how its quality should be evaluated,

- work with engineering to productionize it,

- speak with early users,

- launch it,

- measure adoption,

- and recommend what we should do next.

Product Manager – AI/GenAI

Our AI platform that lets people ask questions of their enterprise data in natural language and get trustworthy answers. The hard part isn't the model — it's the fit between what the product can genuinely do and what customers actually need, over data that is never as clean as anyone hopes. We're hiring a Product Manager to own that fit: to do the detailed thinking on customer pain, competitive landscape, and where the technology is going, and to turn it into a sharp, defensible product proposition and roadmap. You'll work closely with the CTO (who currently carries this thinking) and become the person who owns the depth beneath it.

What you'll own

• Proposition & positioning. Take fuzzy capability and turn it into a clear, honest, defensible articulation of what Jiva is, who it's for, and why it wins. Kill the gap between what's sold and what's deliverable.

• Customer intelligence (within the current structure). Sales and Delivery own the customer relationship today; you build the customer picture through them —mining PoC post-mortems, win/loss, readiness audits, sales-call notes, and Delivery's ground truth. Extract real jobs-to-be-done, separate stated wants from underlying pain, and bring a synthesized customer voice into the room. Over time, earn a path to direct customer contact — but you'll start by getting more 

out of the signal the field teams already carry than anyone does today.

• Competitive & technology intelligence. Track the space — RAG, text-to-SQL, tabular/relational foundation models, agentic systems, the commercial players — and synthesize it into product direction. Know what's real and what's marketing.

• Requirements & roadmap. Translate the above into crisp requirements and a prioritized roadmap, in partnership with Engineering. Make the trade-offs explicit.

• Sales & Delivery enablement. Own the "what we can honestly promise" story so field teams stop overselling and start qualifying. You are the source of truth on capability boundaries.

Must-haves

• 5+ years in B2B / enterprise product management, ideally on data, analytics, AI/ML, or platform products.

• Demonstrated ability to frame a proposition — you can show us a product or feature where you turned raw capability into a positioning that won customers. Portfolio evidence, not a claim.

• Technical fluency in AI/data. You understand RAG, LLMs, text-to-SQL, and data quality well enough to judge feasibility vs. hype and to earn engineers' respect. You don't need to code; you need to reason.

• Customer discovery skill — and the resourcefulness to apply it 

indirectly. You've run structured discovery and can distinguish what customers say from what they need. Just as important here: you can extract sharp customer insight secondhand — through sales and delivery teams, deal histories, 

and product usage — because you won't have direct customer access on day one.

• Evidence-driven and structured. You validate before you evangelize. Strong written thinker — you can put a proposition on one page and defend it.

Nice-to-have

• Exposure to NL→SQL, semantic layers, knowledge graphs, or enterprise data governance.

• 0-to-1 or turnaround experience — comfort building the function, not just running it.

• Experience selling into or building for regulated / data-sensitive enterprises

Role Overview

We are looking for an experienced AI/ML Engineer with strong expertise in Python, Generative AI, LLMs, LangChain, and LangGraph. The candidate will be responsible for designing and developing AI-powered applications, intelligent agents, and scalable LLM-based solutions.

Key Responsibilities

• Design and develop AI/ML and Generative AI applications using Python and modern LLM technologies.
• Build LLM-based applications and AI agents using LangChain and LangGraph.
• Develop agentic workflows involving tool calling, memory, reasoning, and multi-step orchestration.
• Integrate LLMs such as OpenAI, Azure OpenAI, Anthropic, Gemini, or other foundation models.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases.
• Work with embeddings, prompt engineering, semantic search, and document processing.
• Develop scalable APIs and backend services using Python, FastAPI, or Flask.
• Build and integrate AI solutions with existing applications and enterprise systems.
• Deploy and maintain AI/ML solutions on cloud platforms.
• Collaborate with data scientists, software engineers, and product teams to develop business-focused AI solutions.

Required Skills

• Strong hands-on experience in Python programming.
• Strong experience in AI/ML and Generative AI.
• Hands-on experience with LLMs and LLM-based application development.
• Strong experience with LangChain and/or LangGraph.
• Experience building AI Agents / Agentic AI workflows.
• Strong understanding of RAG, embeddings, vector databases, and prompt engineering.
• Experience with vector databases such as FAISS, Pinecone, Chroma, Weaviate, or Azure AI Search.
• Experience developing REST APIs using FastAPI/Flask.
• Good understanding of Machine Learning, NLP, and deep learning concepts.
• Experience with Azure / AWS / GCP cloud platforms.

Good to Have

• Experience with multi-agent systems and agent orchestration.
• Knowledge of MLOps / LLMOps.
• Experience with Docker, Kubernetes, and CI/CD.
• Knowledge of LLM evaluation, monitoring, observability, and AI governance.
• Experience with Azure OpenAI, Azure AI Foundry, or AWS Bedrock.

Applied AI Engineer

About the Role

We are building an AI-powered platform that transforms credit origination by automating manual processes and enabling lenders to underwrite more loans with greater accuracy and lower risk.

We are looking for an experienced Applied AI Engineer to design and build production-grade Agentic AI systems capable of complex, multi-step reasoning across financial data, legal documents, and other unstructured information.

As an early engineering team member, you will take ownership of core AI architecture, multi-agent orchestration, reasoning workflows, evaluation frameworks, and scalable AI infrastructure.

Key Responsibilities

• Design and develop stateful, multi-agent AI systems for complex reasoning and decision-making.
• Build and optimize LLM pipelines using frameworks such as LangChain, DSPy, and GEPA.
• Implement RAG, prompt optimization, memory management, tool integration, and human-in-the-loop workflows.
• Develop AI evaluation frameworks using LLM-as-a-Judge and scenario-based testing.
• Implement AI observability and monitoring to track accuracy, latency, cost, and system performance.
• Build ETL pipelines and work with structured and unstructured data, SQL/NoSQL databases, vector databases, and graph-based data models.
• Integrate external tools and systems using modern agent protocols such as Model Context Protocol (MCP).
• Deploy and maintain AI services on AWS or Azure.
• Implement CI/CD pipelines, containerization, and Infrastructure as Code for reliable production deployments.

Required Qualifications

• 5+ years of professional software development experience in Python or JavaScript.
• Proven experience building and deploying production-level Agentic AI systems or complex LLM reasoning applications.
• Hands-on experience with LangChain and LLMOps, including tools such as Langfuse, Weights & Biases (W&B), or Helicone.
• Strong understanding of LLMs, RAG, prompt engineering, multi-agent orchestration, memory management, and tool calling.
• Experience with DSPy, GEPA, or advanced LLM inference and optimization techniques.
• Strong data engineering fundamentals, including ETL, SQL/NoSQL databases, and vector databases.
• Familiarity with MCP and human-in-the-loop workflows.
• Experience with AWS or Azure and modern DevOps practices, including Docker, CI/CD, and Infrastructure as Code.

Good to Have

• Experience with RLHF or RLAIF.
• Knowledge of model fine-tuning techniques such as LoRA and QLoRA.
• Experience with graph databases and graph-based data models.
• Exposure to financial services, credit underwriting, risk analysis, or document-intensive AI applications.

Why Join Us?

• Work on advanced Agentic AI and complex reasoning systems beyond conventional chatbot applications.
• Build foundational AI architecture and production-grade LLM workflows.
• Take ownership of technical decisions and system design as an early engineering team member.
• Solve challenging problems at the intersection of AI, finance, and intelligent automation.

Strong Lead AI Engineer Profile with agentic systems architecture expertise

2

Mandatory (Experience 1): Must have 6+ years of software or ML engineering, with at least recent 2+ years building and shipping production LLM or agentic AI systems

3

Mandatory (Tech skill 1): Must have hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI), with experience debugging framework internals

4

Mandatory (Tech skill 2): Must have direct SDK experience with Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder

5

Mandatory (Tech skill 3): Must have Python mastery — production-quality code, type annotations, unit and integration tests, packaging, and performance profiling

6

Mandatory (Tech skill 4): Must have RAG pipeline depth — embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses

7

Mandatory (Tech skill 5): Must have cloud deployment experience with AWS, Azure, or GCP; Docker, Kubernetes, IaC basics (Terraform or CDK), and CI/CD pipelines

8

Mandatory (Tech skill 6): Must have agent observability experience — LangSmith, Helicone, or equivalent — with ability to diagnose latency, cost, and quality issues in production traces

9

Mandatory (Tech skill 8) : Must have owned full sub-system designs — agent topology, data flows, API contracts, failure handling — for a bounded scope, and built multi-agent graphs, tool orchestration, retrieval, and memory systems.

10

Mandatory (Tech skill 7): Must have track record of shipping 2+ agentic or ML systems to production — not just proof-of-concepts — with documented performance benchmarks

11

Mandatory (Communication): Must be able to write crisp component specifications and communicate architectural trade-offs to both engineers and non-technical stakeholders

12

Mandatory (Note 1) : Role is Hybrid, WFH flexibility as well upto 6 days a month

Support with design and build to prove out agentic AI solution flow by working with other data 

scientists and engineers to build, train Large Language Model (LLM) architectures, RAG 

systems, and autonomous agentic workflows 

Key qualifications: 

>> AI solution design & Development: Design Agentic AI solutions using RAG (Retrieval-

Augmented Generation) and orchestration frameworks like LangGraph or LangChain. 

>> Model Fine-Tuning: Solid understanding and experience with Pre-train, fine-tune, and 

optimize open-source like BERT, LLama, and other proprietary foundation models for domain-

specific tasks 

>> Solid Stats and ML foundations and (vibe) coding skills with Python, PySpark 

>>  Implement validation frameworks and tracing practices (using tools like Arize) to monitor 

agent behavior, guard against model drift, and ensure compliance 

>> Collaborate with Engineering to deploy models securely on cloud and on-prem ecosystems 

Senior Product Manager – AI / B2B SaaS

Location: Bengaluru

Work Mode: Work from Office

Experience: 5+ years

Department: Product Management

About LeadSquared

LeadSquared is a leading Sales CRM for high-velocity revenue teams, helping businesses manage leads, sales processes, customer interactions, marketing, and revenue workflows at scale.

We serve businesses where large sales teams manage high volumes of leads and customer interactions, making speed, prioritisation, automation, and effective sales execution critical to revenue growth.

Role Overview

We are looking for a Senior Product Manager – AI to own the roadmap and end-to-end execution of AI products within LeadSquared.

Key Responsibilities

• Own the roadmap, prioritisation, and end-to-end execution for your AI product area.
• Understand customer problems and identify where AI can create meaningful, scalable value and translate them into clear product requirements and scalable solutions.
• Drive products from discovery and 0→1 development through launch, adoption, and iteration.
• Build and scale end-user-facing AI experiences that work reliably within enterprise workflows. 
• Make informed product decisions considering quality, latency, and scale trade-offs in production AI systems.
• Work closely with Engineering, Design, AI/ML, Data, and GTM teams to ship high-quality products.
• Define and track product adoption, engagement, customer outcomes, and business impact.
• Drive GTM, adoption, and continuous product optimisation.

What We're Looking For

• 5+ years of Product Management experience.
• 2+ years of hands-on Product Management experience building AI products.
• Strong B2B SaaS product experience.
• Experience in 0→1 product development and launches.
• Strong product discovery, prioritisation, roadmap management, and execution skills.
• Familiarity with AI evaluations (evals), observability, guardrails, and human oversight.
• Data-driven approach with strong understanding of product metrics.
• Excellent communication, problem-solving, and stakeholder management skills.
• Candidates from Tier 1 / Tier 1.5 / Tier 2 colleges preferred.

Good to Have

• Experience building products using GenAI, LLMs, RAG, AI Agents / Agentic AI, or ML-based decision systems.
• Experience in CRM, SalesTech, MarTech, or Enterprise SaaS.
• Experience building AI products for sales, revenue, operations, or other enterprise users.
• Experience taking an AI capability from an early prototype to a reliable, scalable enterprise product.
• Candidates who have built AI side projects will have a preference. Share links to live products, prototypes, demos, GitHub repositories, agents, or apps you’ve built.
•  

About LeadSquared

LeadSquared is a leading sales execution and marketing automation platform trusted by 2,000+ businesses globally, including healthcare, education, financial services, and real estate. Headquartered in Bengaluru with offices across the US, UK, UAE, and Southeast Asia, we empower sales teams to close faster, smarter, and at scale.

Our AI team is at the forefront of integrating cutting-edge large language model capabilities into enterprise workflows — building intelligent agents, copilots, and automation systems that redefine how businesses operate.

Role Overview

We are looking for a Senior AI Engineer with hands-on experience building LLM-powered agents and agentic AI systems. You will design, develop, and deploy autonomous AI pipelines that solve complex, multi-step business problems — from lead qualification and follow-up automation to intelligent CRM workflows and beyond.

This role is ideal for someone who is deeply excited about the frontier of AI, can move fast, and wants their work to directly impact millions of sales professionals worldwide.

Key Responsibilities

•

Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.

•

Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.

•

Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.

•

Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.

•

Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.

•

Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.

•

Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.

•

Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.

•

Contribute to internal best practices, documentation, and knowledge-sharing across the engineering org.

Required Qualifications

Experience

•

2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.

•

Proven experience shipping LLM-based products or agentic AI systems into production environments.

Technical Skills

•

Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.

•

Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).

•

Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.

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Solid understanding of RAG architectures, embedding models, and semantic search.

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Experience with vector databases and similarity search infrastructure.

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Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).

Problem-Solving & Mindset

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Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.

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Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.

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Ability to balance research exploration with engineering pragmatism to ship reliable systems.

Preferred Qualifications

•

Experience with multi-agent orchestration and agent memory systems (short-term and long-term).

•

Familiarity with fine-tuning or RLHF workflows for domain adaptation.

•

Background in NLP, information retrieval, or conversational AI.

•

Prior experience in B2B SaaS or CRM domain is a plus.

•

Contributions to open-source AI/ML projects or published research/blogs.

•

Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services

Product Engineer — Role Summary

We are looking for a Product Engineer to build user-facing products that combine AI capabilities with practical applications. You will work at the intersection of software engineering and AI, developing autonomous, agent-driven systems that solve complex educational and research problems.

Key Responsibilities:

• Product Development: Design, build, and deploy production-ready applications powered by LLMs and AI agents.
• Data Engineering: Build scalable ETL/ELT pipelines to process structured and unstructured data, including text and audio, for RAG and model fine-tuning.
• Agentic Workflows: Develop multi-step AI agents with tool calling, APIs, databases, search, reasoning, and memory.
• Rapid Prototyping: Turn ideas and research concepts into interactive, production-ready applications.
• AI Integration: Use frameworks such as LangChain, LlamaIndex, AutoGen, or custom orchestrators to integrate AI into scalable systems.
• User Experience: Transform raw AI outputs into reliable, intuitive, and responsive user experiences.
• Collaboration: Work closely with ML researchers and data engineers to integrate custom and fine-tuned models.
• Observability: Monitor agent behavior, manage edge cases, reduce hallucinations, and improve reliability in production.

The ideal candidate combines strong software engineering, AI/LLM expertise, data engineering, and product thinking, with the ability to take an AI concept from prototype to production.