Technical AI Product Manager (Agentic AI)

About VerbaFlo

VerbaFlo.ai is a fast-growing AI SaaS startup building a next-generation AI automation platform for real estate, PBSA, and enterprise teams — powering conversations, workflows, campaigns, and decision-making across channels. As a part of our dynamic team, you’ll work alongside industry leaders and visionaries to drive innovation and execution across multiple functions, as VerbaFlo scales rapidly across the UK, EU, and the US.

Role Overview

We are looking for a sharp Product Manager to help scale and evolve VerbaFlo’s AI automation platform, which is already live with real estate, PBSA, and enterprise customers across multiple markets. You will work closely with engineering, AI, design, and customer-facing teams to identify high-impact problems, define solutions, and ship features that deepen the value customers get from VerbaFlo every day. This is an ideal role for an experienced product manager who is comfortable going deep on technology, can own significant areas of the platform end to end, and wants to help shape product direction at a fast-scaling AI company.

Responsibilities

• Own the end-to-end product lifecycle for features and modules, from problem discovery to launch and iteration.
• Scale and evolve the core platform as our customer base grows, balancing new capabilities with reliability.
• Conduct customer discovery through interviews, call reviews, and feedback sessions across the UK, EU, and US.
• Translate customer and business needs into clear PRDs, user stories, and acceptance criteria.
• Prioritise the roadmap using data, customer impact, and business value, and communicate trade-offs clearly.
• Work closely with engineering on scoping, technical trade-offs, sprint planning, and delivery.
• Partner with AI/ML engineers to define use cases for LLM-powered agents, voice, and conversational workflows. Define evaluation criteria for AI features — accuracy, latency, hallucination rates, and conversation quality.
• Write and iterate on prompts, conversation flows, and agent behaviours alongside the AI team.
• Collaborate with design to create intuitive user journeys, wireframes, and prototypes.
• Define success metrics and build dashboards to track adoption, engagement, and customer outcomes.
• Analyse product usage data and conversation logs to identify gaps and opportunities.
• Run experiments and A/B tests to validate hypotheses and improve product performance.
• Coordinate launches with sales, marketing, and customer success, including release notes, demos, and enablement.
• Support integrations with CRMs, property management systems, and third-party platforms used by our customers.
• Gather and synthesise feedback from sales calls, support tickets, and enterprise client reviews.
• Track the competitive landscape and emerging AI capabilities, turning insights into product bets.
• Establish and own lean product processes, documentation, and rituals as the team scales.

Requirements

• 4-5 years of experience in Product Management, ideally in B2B SaaS or AI-first products.
• Track record of owning a product area end to end and driving measurable business outcomes.
• Experience taking a product or feature from 0 to 1 is preferred.
• Working understanding of LLMs, AI agents, APIs, and how modern software systems are built.
• Hands-on experience with AI tools, prompt engineering, or no-code/low-code builders is a plus.
• Ability to write crisp PRDs and communicate clearly with engineers, designers, and business stakeholders.
• Strong analytical skills; comfortable with SQL, spreadsheets, and product analytics tools.
• Customer-obsessed, with a track record of turning user insight into shipped product.
• Exposure to real estate, PBSA, proptech, or conversational AI is a strong plus.
• Comfortable working across time zones with international customers and teams.
• Strong ownership, bias for action, and follow-through on multiple concurrent priorities.
• Sharp, structured problem-solver who is comfortable operating with ambiguity.
• Ambition to grow into a senior product leadership role.

Why Join Us?

• Shape a live AI product used by enterprise customers, with real ownership from day one.
• Work alongside a strong, hands-on engineering and AI team.
• Work across global markets — UK, EU, India, and US.
• Work at the cutting edge of LLMs, voice AI, and agentic automation.
• A clear growth path toward senior product leadership.

If you’re a builder at heart who loves turning messy problems into products customers can’t live without, and you’re excited to help shape the future of AI automation at VerbaFlo, we’d love to hear from you!

Apply here:

https://docs.google.com/forms/d/e/1FAIpQLSfiDMeHRTVFDcP9xDqRKa9hPsgwHWj2qnHzDIKzSYsgDiyltA/viewform?usp=header

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)

Job Description:

We are looking for a hands-on AI Engineer with experience in Generative AI and Agentic AI to build and deploy production-ready AI solutions.

Key Responsibilities:

• Develop and deploy GenAI and Agentic AI applications.
• Build RAG pipelines, LLM workflows, and AI agents.
• Develop solutions using Python, LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Implement tool calling, context retrieval, and LLM orchestration.
• Integrate AI solutions with APIs and cloud platforms.
• Work with AWS/Azure/GCP, Docker, and CI/CD.

Required Skills:

• Strong Python programming skills.
• 3+ years of GenAI/Agentic AI experience.
• RAG and LLM orchestration.
• LangChain / LangGraph / LlamaIndex / AutoGen / CrewAI / Semantic Kernel.
• MCP and A2A knowledge.
• Cloud, APIs, Docker, and CI/CD experience.

Preferred Experience:

Hands-on experience building and deploying production-ready AI solutions.

-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.

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 

AI Engineer

LLMs, Agents & AI Services

📍 Mumbai (On-site) | Full-time | 2-4 years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

AI is core to how we design, deliver, and scale software for our customers.

We are hiring an AI Engineer for a dedicated client engagement building a complex production AI platform, working on the AI capabilities and agentic features at the core of the product.

The mandatory requirement for this role is at least one AI feature personally shipped to production for real users, with operational ownership.

The role suits someone who thinks quickly on solutioning, can take an ambiguous problem to a working prototype in days, and has the discipline to carry it through to production with predictable economics.

You will work alongside the Senior AI Engineer and the wider pod, with ownership of parts of the AI surface area of the product.

Responsibilities:

Solutioning and POCs

Translate ambiguous customer problems into working POCs at speed.

Pick the right model, framework, and architecture, and demonstrate value early before scaling investment.

LLM Application Development

Build AI features and services using LLM APIs from OpenAI, Anthropic, Google, and self-hosted open-weight models (Llama, Qwen, Mistral).

Choose the right model per use case based on cost, latency, capability, and context-window trade-offs.

Agentic System Design

Design and implement agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, or custom orchestration.

Cover tool use, planning, memory, and multi-step reasoning appropriate to the problem.

API and Service Development

Build production AI services and APIs using Python and FastAPI.

Handle streaming responses, async processing, structured outputs, retries, and graceful degradation when models or tools fail.

Retrieval and Tool Integration

Implement RAG pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma), embeddings, chunking strategies, hybrid search, and reranking.

Integrate external tools, internal APIs, and document sources through tool-calling and MCP-style patterns.

Cost Analysis and Unit Economics

Model the per-request and per-user cost of every AI feature before it ships.

Track token usage, prompt caching, batching, and model-routing strategies.

Drive measurable improvements in unit economics.

Production Hardening

Add observability and tracing (LangSmith, Langfuse, OpenTelemetry), guardrails, content safety checks, prompt injection defences, and fallback behaviour.

Prompt Engineering and Evaluation

Design, test, and iterate prompts with measured outcomes.

Build evaluation harnesses for accuracy, hallucination, latency, and cost.

Run benchmarks across models and prompt variants before locking in a design.

Requirements:

AI Feature Shipped to Production (Mandatory)

Must have personally built and shipped at least one AI feature that runs in production for real users, with operational ownership.

POCs, internal demos, and one-off scripts do not qualify.

2 to 4 Years of Professional Software or AI Engineering Experience

With at least one production AI feature owned end to end.

Strong Python Proficiency and API Development with FastAPI

Comfort with type hints, async, packaging, testing, streaming responses, and authentication.

Production-grade Python, not notebook-only code.

Hands-on Depth Across the LLM and Agent Stack

Working experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or self-hosted open-weight models (vLLM, Ollama, Together, Replicate).

Working familiarity with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Working knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma).

Solutioning Speed and POC Velocity

Demonstrated ability to move from a fuzzy problem to a working prototype in days.

Strong instinct for what to build first, what to defer, and what to throw away.

Cost Discipline for Production AI

Ability to calculate, monitor, and optimise the cost of LLM APIs, tokens, embeddings, vector store usage, and infrastructure.

Treats unit economics as a first-class concern.

AWS Familiarity

Working knowledge of EC2, S3, IAM, and at least one of Bedrock, SageMaker, or equivalent.

Comfortable in a Fast-Moving Environment

Self-directed, comfortable with ambiguity, takes ownership without being asked, and ships under shifting priorities.

Strong Written and Spoken English Communication

Able to explain trade-offs to non-AI engineers, designers, product managers, and clients in plain language.

Nice to Have

• fine-tuning or LoRA, QLoRA, PEFT exposure
• MCP server authoring
• eval framework experience (LangSmith, Promptfoo, Ragas, DeepEval)
• open-source AI contributions
• multi-modal models (vision, audio)

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.

•

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

•

Experience with vector databases and similarity search infrastructure.

•

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

Problem-Solving & Mindset

•

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

•

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

•

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