Convosight

What we are looking for

 3–8 years of experience in consumer insights, market research, strategy consulting, social intelligence, cultural insights, or a related research environment.

 Experience independently owning custom research projects—from ambiguous brief through analysis,

synthesis, recommendation, and client presentation.

 Strong qualitative and quantitative reasoning. You can work with both nuance and numbers without

overstating what the data proves.

 Exceptional PowerPoint or Google Slides craft: storyline, executive writing, information hierarchy, charts, visual examples, and disciplined editing.

 A portfolio of client-ready research decks or case studies that demonstrates synthesis and

storytelling—not merely attractive slide design.

 Confident client presence and clear spoken communication, including experience presenting to senior

stakeholders and navigating difficult questions.

 Hands-on fluency with modern AI tools for research and creative production. You know how to

prompt, critique, verify, refine, and combine outputs into work that does not feel machine-generated.

 High ownership, speed, curiosity, and composure in a startup where priorities can move quickly and

important projects can arrive with short timelines.

You will stand out if you have

 Experience in beauty, personal care, food and beverage, nutrition, home care, or another consumer category.

 Agency-side experience managing multiple clients and deadlines without sacrificing quality.

 Experience with social/video intelligence, digital ethnography, online communities, search, reviews, or

creator/influencer data.

 A strong visual instinct and comfort using image-generation or design-assistance tools to make

abstract insights tangible.

 Experience leading or mentoring researchers, even if you have not formally managed a large team.

Must-have

• LLB or LLM from a recognised institution

• 2–4 years of hands-on corporate / transactional legal experience

• Prior experience in a startup, VC-backed company, or fast-paced legal setup (not just largelaw firms)

• Proven ability to draft and redline contracts independently

• MCA / ROC filings done without supervision

• Strong written English — your documents represent Convosight externally

• Highly organised — zero misses on deadlines and compliance calendars

• Ability to work across teams without needing hand-holding

Good to have

• Experience supporting a fundraise round (Series A / B due diligence preferred)

• Working knowledge of DPDP Act, GDPR basics, or data privacy frameworks

• Exposure to global client contracts (US / EU counterparties) • Basic familiarity with SEBI / FEMA regulations

• Comfortable with DocuSign, contract lifecycle tools, or Notion / Jira for task tracking

Requirements:

• 5+ years in data architecture/data engineering, with at least 2+ years in an architect or lead capacity.
• Strong SQL: advanced query optimisation, indexing, partitioning strategies.
• Data modeling dimensional modelling (star/snowflake schema), normalization/denormalization tradeoffs, entity relationship design.
• Cloud data platforms: hands-on with AWS (Redshift, S3 Glue), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow).
• Big data ecosystems: Spark, Hadoop, or Kafka for large-scale/streaming data.
• Data warehousing Snowflake, Redshift, BigQuery, or Databricks.
• ETL/ELT pipeline design: Airflow, dbt, Fivetran, or similar orchestration tools.
• Data governance & security: data lineage, access control, compliance (GDPR/SOC2), master data management.

Strongly Preferred:

• Experience architecting systems supporting ML/AI pipelines (feature stores, vector DBs, real-time inference data flows).
• Programming in Python or Scala for pipeline development.
• API/microservices architecture exposure, understanding how data systems integrate with application layers.
• Experience with data mesh/data lake house architectures.
• Prior experience presenting architecture decisions to leadership/stakeholders.

Nice-to-Have (Differentiators):

• Certifications: AWS/GCP/Azure data architecture certs.
• Experience in a high-growth startup (built systems from scratch, not just maintained legacy).
• Exposure to real-time/streaming architecture (Kafka, Kinesis, Flink).

AI Product QA Engineer

This is not the QA you know. We're not hiring someone to find bugs. We're hiring someone to guard the user's experience — with AI as the foundation.

Read this before you read anything else

Forget everything traditional QA taught you.

I don't want "I tested it, here are the 14 defects." I don't want a gatekeeper at the end of the line ticking checkboxes against a technical feature list. That QA is dead here, and honestly, AI already does it better.

I want business-product QA. Product taste as a discipline. User empathy as a test case. Your foundation is AI — everything runs through Claude Code / Codex — and on top of that foundation you apply QA from a business, user-empathy, onboarding, and go-to-market lens. You're not asking "does the button work?" You're asking "is this the sharpest user journey this product could possibly have?"

We're extreme about four things and nothing else: extreme hard work, extreme respect, extreme shipping, extreme innovation. Not hours . There's no boss — there's a product, a user, and you. You're rewarded on one thing: your hunger.

The 90 / 10

Same philosophy that runs this whole company. 90% of the work, AI does. Claude Code writes the test cases, automates the suites, drives the API calls, spins up the harness — faster than any QA team I've ever run . That 90% is the floor now.

The 10% is you — the human who decides what's actually worth testing and why:

• the user empathy — feeling the friction a real brand manager feels at 9pm
• the judgment — is this the sharpest journey, or just a working one?
• the taste — the design standard, the elegance, the delight
• the business sense — does this survive a real client's Tuesday, their onboarding, their objection?
• the critical eye — reading an AI trace and knowing instantly when the product is confidently wrong

AI didn't shrink QA. It concentrated it. The 10% is smaller and heavier than the 100% ever was. That's the 10% I'm hiring.

How you actually work

Everything you do runs through Claude Code. You write the cases in it. You automate the cases in it. You do business testing in it. You interrogate user empathy in it — "how will the user react to this, and is this the sharpest journey of the product?"

But make no mistake: this is technical QA. You write scripts. You hit and test APIs directly. You read the traces, the payloads, the failure modes. You don't wait for a UI to click — you go straight at the system. The difference from old-school QA isn't less technical; it's technical in service of the user and the business, not the feature checklist.

The stack & the surface

We're a web application in Python, Rust, and everything in between — and you'll be QA-ing across four products: ARIA, the Persona product, the video-decoding product, and the rest of the family. Real APIs, real pipelines, real agents watching millions of social videos in Hindi, Tamil, Bahasa, Thai for the world's biggest consumer brands . You need to be comfortable writing a script to probe any of them and reading exactly what came back.

Your first 48 hours

Day one, morning: you get access to a live product and its real client questions in the queue. Not a sandbox. Not a tutorial.

Day one, afternoon: you pick one user journey and, in Claude Code, you build the case, automate it, and pressure-test it against the actual experience a client would have.

Day two: you tell me not "here are the defects" but "here's where the journey breaks the user, and here's the sharper one." And you've already got the automated eval that guards it going forward.

If that scares you, this isn't your room. If it makes you grin — keep reading.

The loop

We are always on. A technique drops on Twitter Tuesday morning; by Thursday it's in a product; by Friday a brand team on the other side of the world is using it — and someone has to make sure that journey is delightful, not just functional. That someone is you. See it. Test it against the user. Sharpen it. Ship it. The loop never stops. If your learning lives in "read later" folders , this will break you.

What you own

The user's experience across every product. Not the defect list — the journey. Find a problem and you don't just log the instance; you build the automated eval in Claude Code that kills the whole class of bad experience forever. Your evals become the product's conscience. Your standard becomes the bar the whole team ships to.

The bar is craft, not years. 23 or 43 — I don't care. I care whether you can feel what a user feels and write the script that proves it.

Who this is actually for

This is a close-to-100% self-starter environment.

You do not want to be here if you want to be handed a test plan. You want to be here if you believe you're the only one who can guard this product's soul — the king or queen of the user journey — with absolute hunger burning in you to do it. Here, will is bigger than skill. And AI isn't a tool you use; it's the foundation you think on.

Fair warning ⚠

The pace is relentless and there's no playbook for AI-product QA — you're partly inventing the discipline as you go. If you want a stable checklist and a comfortable gate at the end of the pipeline, you'll be miserable here, and I'd rather you know now. But if you want to define what QA even means in the age of agents — what you learn here in one year, you won't learn anywhere else in five.

How this goes

Don't send a resume. Send the 10%.

An eval harness you're proud of. A test suite you built in Claude Code that caught something a checklist never would. A teardown of a product's user journey where your taste and your technical chops were unmistakably yours.

Work Location: Remote

About the job

Must-Have Skills

• 5+ years in data architecture / data engineering, with at least 2+ years in an architect or lead capacity
• Strong SQL — advanced query optimization, indexing, partitioning strategies
• Data modeling — dimensional modeling (star/snowflake schema), normalization/denormalization tradeoffs, entity relationship design
• Cloud data platforms — hands-on with AWS (Redshift, S3, Glue), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow)
• Big data ecosystems — Spark, Hadoop, or Kafka for large-scale/streaming data
• Data warehousing — Snowflake, Redshift, BigQuery, or Databricks
• ETL/ELT pipeline design — Airflow, dbt, Fivetran, or similar orchestration tools
• Data governance & security — data lineage, access control, compliance (GDPR/SOC2), master data management

Strongly Preferred

• Experience architecting systems supporting ML/AI pipelines (feature stores, vector DBs, real-time inference data flows)
• Programming — Python or Scala for pipeline development
• API/microservices architecture exposure — understanding how data systems integrate with application layers
• Experience with data mesh / data lake house architectures
• Prior experience presenting architecture decisions to leadership/stakeholders

Key Responsibilities

• Own and drive the product roadmap for data and AI-powered features end-to-end

• Translate complex data and AI capabilities into simple, intuitive product experiences for brands and creators

• Collaborate closely with data engineers, ML engineers, and business teams to define and deliver product requirements

• Define product metrics and success criteria — including data quality, model accuracy, and user engagement

• Identify gaps in existing data pipelines and work with engineering to build scalable solutions

• Conduct user research, competitor analysis, and market mapping to inform product decisions

• Prioritise features and manage the product backlog with a strong data-driven approach

• Work with the AI team to evaluate and integrate LLM and GenAI capabilities into the product

Must-Have Skills

• Total experience of 2–3 years with at least 1–2 years in data engineering or data analytics

• Transitioned into or actively pursuing a product management role

• Strong understanding of data pipelines, ETL processes, SQL, and data modelling

• Ability to write and interpret data queries to inform product decisions

• Excellent communication and stakeholder management skills

• Strong product thinking — ability to break down complex problems into simple product solutions

Who We Are

We’re a small, global team shipping ambitious features at a pace that would overwhelm most.

The kind of pace where QA isn’t a back office function — it’s the heartbeat of everything we

ship. Our customers expect speed and trust. QA is where those two meet.

What We Need

We’re looking for a QA Specialist who thrives in fast-moving environments. Someone who can:

● Spot gaps before they become failures.

● Shift gears as priorities change — without losing accuracy.

● Push testing forward with the urgency of a live launch.

This isn’t about ticking boxes on a test plan. It’s about owning the quality of what reaches the

customer, no matter how quickly the roadmap evolves.

Your Craft

● 3–5 years of experience in software QA (manual, automated, or both).

● Fast learner who can get inside the product’s mind and test like a real user.

● Ability to design and execute test cases at lightning speed — without compromising

depth.

● Comfort with dynamic priorities: you can validate a demo at noon and regression test a

release by 6pm.

● Familiarity with QA tools, automation frameworks, and bug tracking — but more

importantly, the instinct to know what needs to be tested first.

Key Responsibilities

Backend Development

• Design, develop, and maintain scalable backend services using Rust, Python, and TypeScript.
• Build high-performance microservices and APIs for AI-driven products.
• Develop fault-tolerant, secure, and maintainable distributed systems.
• Design event-driven architectures and asynchronous processing pipelines.
• Implement caching, message queues, and database optimization strategies.

AI & Machine Learning Integration

• Build and maintain backend infrastructure supporting LLMs, Generative AI, RAG systems, and AI Agents.
• Integrate models from providers such as OpenAI, Anthropic, Gemini, and open-source LLMs.
• Develop vector search solutions using Pinecone, Weaviate, Qdrant, or similar technologies.
• Build AI workflows for content generation, summarization, sentiment analysis, and conversational intelligence.
• Optimize AI inference pipelines for performance and cost efficiency.

System Architecture

• Design scalable architectures capable of handling high traffic and large volumes of data.
• Develop real-time processing systems and data pipelines.
• Implement observability, monitoring, logging, and alerting mechanisms.
• Drive performance tuning and latency optimization across services.

Cloud & DevOps

• Deploy and manage applications on AWS, GCP, or Azure.
• Work with Docker, Kubernetes, CI/CD pipelines, and Infrastructure as Code.
• Ensure system reliability, security, and scalability. 

Key Responsibilities:

• Design, execute, and maintain comprehensive manual test cases.
• Perform exploratory, functional, and regression testing across multiple releases.
• Identify, document, and track defects using bug tracking tools (e.g., JIRA).
• Collaborate closely with developers and product teams to ensure timely, high-quality releases.
• Prioritize testing tasks effectively in a fast-paced, dynamic environment.

Required Skills:

• Solid manual testing expertise with strong attention to detail.
• Experience in functional, regression, and exploratory testing.
• Proficiency with test management and bug tracking tools.
• Ability to create and execute test cases quickly without compromising depth.
• Strong adaptability to shifting priorities and tight deadlines.

Good to Have (Optional):

• Basic knowledge of automation frameworks (Selenium, Cypress, Playwright, etc.).
• Exposure to CI/CD pipelines and version control (Git, Jenkins, etc.).

Qualifications:

• 3–5 years of professional experience in software QA.
• Bachelor’s degree in Computer Science, Engineering, or related field (preferred).

Core AI Backend Engineer – LLM Fine-Tuning

You know that moment when you don’t just debug code — you train a model, fine-tune it, and suddenly it understands your domain better than you expected? That’s the kind of magic we’re looking for.

We’re building something that turns chaotic social video data into crystal-clear business intelligence. Not just another API — but AI-backed architecture fine-tuned to our world. Systems that marketing teams thank you for, because they feel the intelligence, not just the infrastructure.

Either you feel the craft when you read this, or you don’t. This isn’t just another backend role. This is where you bring together scalable systems and cutting-edge LLMs to build something the world hasn’t seen before.

Who We Are

We’re a small, global team that ships fast. Every line of code and every model choice affects millions of video analysis requests.

Our engineers don’t just build APIs — they architect solutions, they optimize at scale, and now, they fine-tune models to make AI work in the real world. Our CPTO still codes. Our senior engineers make complexity look effortless. Our backend team sets a standard that others ask how we move so fast.

What We Need

We need someone who’s lived both sides of this life:

• Backend excellence: building high-scale, high-performance systems.
• LLM fine-tuning: hands-on with open-source models, not just calling APIs.

Someone who can sit with a requirement at 2pm and by 6pm not only has endpoints working, but also has a fine-tuned model running behind them — customized to our use case.

Your Craft

• JavaScript/TypeScript & NodeJS as core backend tools.
• Next.js for full-stack where needed.
• Rust when performance is non-negotiable.
• Golang/Python as comfortable tools of choice.
• MySQL/Postgres/Redis — wielded with intention.
• AWS ecosystem — your playground, not your puzzle.
• LLM/AI integration you’ve actually shipped.
• Open-source LLM fine-tuning experience:
• Bringing in open-source models (LLaMA, Mistral, Falcon, etc.).
• Fine-tuning/adapting them for specific domains.
• Optimizing for inference cost, latency, and accuracy.

The Reality

The work is beautifully complex. The scale is real and growing. The problems are the kind that wake you up at 3am with solutions.

If you get your energy from building backend systems and adapting LLMs to make them smarter for real-world use, you’ll probably fall in love with what we do. If you’re only interested in APIs without touching models, this won’t be your thing — and that’s completely okay.

How to Apply

If you’re reading this thinking “finally, a team that actually cares about real AI engineering” — we’d love to see something you’ve built.

Not just a resume. Show us your craft:

• An LLM fine-tuning repo.
• A domain-adapted model you worked on.
• A system design where you combined backend and AI.
• Or even a short write-up or voice note explaining what you’ve fine-tuned.

We’re genuinely excited to see what you’ve done and have a meaningful conversation about whether this could be magic for both of us.