MindBridge

Senior Technical Manager

Primary objective: Own engineering teams, architecture, delivery governance and successful execution of product and AI initiatives across DocTutorials.

Location

Hyderabad / Chennai

Employment

Full-time

Experience

10+ years

Role Summary

The Senior Technical Manager combines technical leadership, people management, product delivery and project execution. The role is accountable for team outcomes, engineering quality, timelines, production stability and the disciplined delivery of AI projects.

Key Responsibilities

1. Technology and Architecture Leadership

·       Provide direction for Python FastAPI, React/Next.js, data, cloud and AI architectures.

·       Review solution designs for security, scalability, maintainability, performance and cost.

·       Define engineering standards and resolve complex cross-team technical risks.

·       Remain technically hands-on through architecture reviews, critical code reviews and incident debugging.

2. People and Delivery Management

·       Lead, mentor and develop engineering teams with clear goals and accountability.

·       Own estimation, capacity, execution, dependencies, risks, releases and delivery recovery plans.

·       Conduct regular one-to-ones, performance reviews, coaching and skill-development planning.

·       Work with Product, QA, DevOps and stakeholders to deliver measurable product outcomes.

3. AI Project Execution

·       Assess AI use cases for feasibility, data readiness, risk, cost and measurable value.

·       Lead delivery of LLM, RAG, agentic and AI-assisted product initiatives from PoC to production.

·       Ensure evaluation, human validation, security, privacy, observability and rollback controls.

·       Prevent isolated demos, uncontrolled AI usage and dependency on individual engineers or vendors.

Required Skills and Experience

·       10+ years in software engineering with significant technical and people leadership experience.

·       Strong understanding of Python/FastAPI, React/Next.js, APIs, databases, cloud and distributed systems.

·       Proven ownership of product delivery, project planning, risk management and production operations.

·       Experience hiring, mentoring, evaluating and developing engineering teams.

·       Strong knowledge and execution experience across AI/ML, LLM, RAG or agentic-AI projects.

·       Practical understanding of Generative AI, LLMs, RAG, Agentic AI and responsible AI use relevant to the role.

·       Ability to use approved AI tools productively while protecting source code, credentials, learner data and organisational information.

·       Commitment to human review, testing, security validation and traceability for AI-assisted outputs.

Good-to-Have Experience

·       EdTech or medical-learning product experience.

·       AWS, Kubernetes, DevSecOps, MLOps or LLMOps.

·       Experience scaling teams and platforms through major transformation.

How Success Will Be Measured

·       Delivery becomes predictable with risks identified early.

·       Engineering quality, security and production stability improve.

·       AI projects progress from validated use cases to reliable production outcomes.

·       Team capability, ownership, engagement and succession strength increase.

·       Work consistently delivers measurable outcomes and business value—not merely completion of assigned tasks or activities.

Expected mindset: Own the outcome, use AI responsibly, communicate risks early and deliver secure, measurable value for DocTutorials and its learners.

Chief Engineer - Artificial Intelligence

AI Architecture | Production Engineering | LLM, RAG & Agentic AI

Primary objective: Build secure, scalable and reusable AI capabilities that move from experimentation to reliable production use across DocTutorials products and business functions.

Department

Technology & Product Engineering

Location

Hyderabad / Chennai

Reports to

Vice President - Technology

Experience

10+ years in software engineering, including 5+ years in AI/ML

Role Summary

The Chief Engineer - AI will define and lead AI engineering architecture, standards and production implementation. This is a senior, hands-on technical leadership role responsible for building reliable AI platforms and guiding AI engineers, data teams and application teams.

Key Responsibilities

1. AI Strategy and Architecture

·       Define the AI engineering strategy, architecture and technical roadmap.

·       Evaluate and select suitable models, frameworks, vector databases and AI platforms.

·       Make practical build-versus-buy and open-source-versus-commercial decisions.

·       Ensure AI solutions are modular, reusable, scalable, secure and cost-effective.

2. Production AI Engineering

·       Lead AI initiatives from use-case assessment and proof of concept through production deployment.

·       Build reusable AI services, APIs, pipelines and platform components for web, mobile and internal applications.

·       Establish development, testing, deployment, monitoring and production-support practices.

·       Resolve complex issues involving model quality, retrieval, performance, reliability and cost.

3. LLM, RAG and Agentic AI

·       Design LLM applications, enterprise RAG solutions and controlled AI-agent workflows.

·       Build document processing, chunking, embedding, indexing, retrieval and response pipelines.

·       Implement source traceability, human approval and clear permissions for sensitive use cases.

·       Improve response quality through prompt design, retrieval optimisation and evaluation.

4. AI Quality, Security and Responsible AI

·       Define evaluation standards for accuracy, relevance, groundedness, safety, latency and cost.

·       Introduce benchmark datasets and regression testing for prompts, models and retrieval pipelines.

·       Implement safeguards for hallucinations, unsupported responses, data leakage and unsafe actions.

·       Ensure role-based access, encryption, audit logging, secure secrets handling and approved data usage.

5. AI Platform and Integration

·       Create common AI capabilities and integration standards that can be reused across products and teams.

·       Integrate AI services with applications, databases and approved enterprise data sources.

·       Implement observability for model usage, quality, errors, response time and cost.

·       Reduce dependency on a single model provider, individual engineer or external vendor.

6. Technical Leadership and Team Development

·       Define AI engineering standards for architecture, coding, review, testing and documentation.

·       Remain hands-on through design reviews, critical code reviews, debugging and focused prototypes.

·       Mentor AI/ML engineers, data engineers and application teams; support hiring and capability building.

·       Work with Product and business teams to convert high-value problems into measurable AI solutions.

Required Skills and Experience

·       10+ years of software engineering experience, including 5+ years in practical AI/ML engineering.

·       Proven experience moving AI solutions from prototype to reliable production use.

·       Strong Python and production API development experience, preferably FastAPI.

·       Strong knowledge of LLMs, RAG, prompt engineering, embeddings and agentic AI.

·       Experience with vector databases such as pgvector, Qdrant, Milvus or Weaviate.

·       Experience with LangChain, LlamaIndex, Haystack or similar orchestration frameworks.

·       Knowledge of cloud architecture (preferably AWS), microservices, databases, queues and distributed systems.

·       Experience with Docker, Kubernetes, CI/CD, observability and production support.

·       Strong understanding of application security, data privacy and responsible-AI engineering.

·       Demonstrated technical leadership, mentoring and stakeholder-management capability.

Good-to-Have Experience

·       Healthcare, medical education, EdTech or digital-learning platforms.

·       Medical-content intelligence, knowledge graphs, ontologies, taxonomy or semantic search.

·       Clinical Case Library, personalised learning or recommendation systems.

·       Multi-modal AI involving text, audio, images or video.

·       Healthcare-data privacy, de-identification or standards such as SNOMED CT, ICD, HL7 or FHIR.

How Success Will Be Measured

·       Production adoption and measurable business or learner impact of AI capabilities.

·       Accuracy, groundedness, reliability, security and cost efficiency of AI solutions.

·       Reusable AI services delivered across products with reduced technical and vendor dependency.

·       Improved capability, engineering quality and ownership within the AI team.

We are looking for a hands-on AI engineering leader who can distinguish a valuable production capability from an attractive demo and build AI that is secure, scalable, measurable and maintainable.

Founder’s Office Intern — Product & AI

We’re looking for a sharp, detail-oriented student who enjoys building things, takes ownership, and is eager to learn how AI can improve the way we work. You’ll work closely with the founder and engineers to research, automate, test, and ship product improvements.

What you’ll do

• Automate product workflows: Identify repetitive tasks and build practical automations using AI tools and agents.
• Help shape the roadmap: Review upcoming features, research the market and competitors, and recommend what we should build next.
• Track feature delivery: Keep a clear view of features across planning, development, testing, and production. Monitor deployments and flag blockers.
• Check product quality: Test features and user journeys, spot bugs and unexpected behaviour, and work with engineers to get issues resolved.
• Follow through: Document issues clearly, track fixes, and verify that the product works as expected after deployment.

Who we’re looking for

• A professional with strong attention to detail, curiosity, and the motivation to learn AI by doing.
• Some hands-on experience building basic AI automations and using AI agents—for coursework, a personal project, or an internship.
• Someone who can break down problems, communicate clearly, and follow a task through to completion.
• Evidence of commitment and achievement in at least one area. This could be coding, sports, college leadership, competitions, or another pursuit where you’ve put in sustained effort and stood out.

About the Role

We are hiring a Backend Engineer to build and operate the services that power a leading technology marketplace being relaunched under new ownership.

You will work behind the GraphQL federation, inside the ~40 microservices that own billing, subscriptions, the catalog, RBAC, the proxy's control plane, and the Analytics/Quota services. The runtime is Node.js/TypeScript on AWS EKS, with Aurora MySQL / Postgres and DynamoDB for state, Redis / Elasticache / Couchbase for caching, Kafka (MSK) and Kinesis as the event backbone, and ClickHouse for real-time analytics reads. Every consumer-to-provider call passes through the proxy your services configure, and every dollar of marketplace revenue flows through the commerce code your services own.

Roles and Responsibilities

·       Build and operate services — Design, ship, and run Node.js/TypeScript services that sit behind the GraphQL federation. Own a slice of the ~40-service estate end-to-end: API design, schema, persistence, tests, deployment, on-call.

·       GraphQL federation subgraphs — Add types, resolvers, and capabilities to the federated Platform API. Keep subgraph boundaries clean; respect RBAC at the federation enforcement layer.

·       Commerce backbone — Build the subscription, invoicing, overage, dispute, and payout flows that integrate with Stripe and PayPal webhooks and the billing state machine. Make sure metering, billing, and payouts stay accurate, auditable, and replayable.

·       Proxy control plane — Build the management APIs and background workers that configure the runtime proxy: API keys, plans, rate-limit policies, quota caches, abuse signals.

·       Event-driven systems — Produce and consume Kafka and Kinesis events safely: schema evolution, idempotency, retries, dead-letter handling. Operate Lambda consumers where they're the right tool.

·       Data services — Help own the Node.js Analytics Service and Quota Service that front ClickHouse and Snowflake. Keep latency low, queries cheap, and authorisation correct.

·       Performance and reliability — Diagnose hot paths, tune queries, add caches where they matter, and remove caches that have become liabilities. Write the SLOs and the alerts that defend them.

·       Security isolation — Implement OAuth2 / JWT / mTLS where they belong; respect least-privilege; design APIs that fail closed.

·       On-call and incident response — Take part in the rotation. Lead investigations across services when something rooted in your area breaks.

·       LLM-assisted engineering — Use LLM-based coding and agentic tooling to evolve how features are designed, reviewed, and tested.

What Success Looks Like

•    Services you own meet their SLOs, deploy multiple times a day, and rarely wake anyone up.

•    Billing, subscription, overage, and payout flows you ship are accurate to the cent, replayable, and trusted by both providers and consumers.

•    GraphQL subgraphs you maintain stay fast, well-typed, and pleasant to extend.

•    Event pipelines you produce and consume are idempotent, observable, and survive replay and back-pressure.

•    The first generation of agent-native primitives you help build land in production and start carrying real agent traffic.

What You Bring

•    5–8 years of backend engineering experience, with non-trivial work on production services that real customers depended on.

•    Strong TypeScript / Node.js in a production service environment; comfort with async patterns, streams, and back-pressure.

•    Solid API design — REST and GraphQL (ideally federation: Apollo Federation, Mercurius, or similar). Schema design, persisted queries, performance tuning.

•    Production fluency with SQL — MySQL and/or Postgres. Bonus for Redis or ClickHouse.

•    Experience with event-driven systems — Kafka or similar. You understand idempotency, exactly-once myths, and consumer lag.

•    Hands-on AWS (EKS, MSK, Kinesis, DynamoDB, Lambda, DMS) and Kubernetes. Comfortable reading and writing Terraform.

•    Modern engineering practice: observability, feature flags, CI/CD, and safe rollouts.

•    Care for correctness on the money path — you have shipped billing, metering, or payment code before and have the scars to show for it.

•    Bonus: experience with API gateways, rate limiting, quota enforcement, usage metering, agent protocols (MCP, A2A, x402), or LLM-based developer tooling.

•    You write small, reviewable PRs. You add the test before you fix the bug. You would rather ship a smaller thing well than a bigger thing badly.

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will drive the research behind our imaging products, finding approaches to hard, unsolved problems in product and apparel imagery that work at production scale. You will run the experiments, prove what is viable, and hand a working approach to the engineering team.

Who We Are Looking For

•     Total experience: 3 years or more, with a strong research orientation

•     Deep learning frameworks in Python: PyTorch or TensorFlow

•     Image processing in Python: OpenCV, Pillow, scikit-image

•     Working knowledge of diffusion and other image generation models

We are looking for a strong research or research-student profile: someone who investigates, experiments and proves an approach, working closely with the AI Architect. Someone who is driven to build solutions, not just desk research.

AI Skills and Experience

•     Computer vision: classical CV alongside deep learning.

•     Segmentation, image-to-image translation, geometry and lighting; 

•     Generative imaging: diffusion models, conditioning and control, fine-tuning and LoRA,

•     Reads academic papers, judges what is reproducible, and turns one into a working prototype in days

Good to have

•     3D and rendering; published research or open-source contributions; model optimisation for inference cost

Research and innovative problem solving

•     Comfortable where there is no known answer, and defines the approach yourself

•     Solves problems inventively rather than reaching for the biggest model; many results come from classical image processing, fitment and geometric transformation

Other Relevant Skills and Experience

•     Designs experiments: baselines, measurable success criteria, honest reporting of negative results

•     Explains findings to a non-research audience and guides engineers to production

•     Git and reproducible experiment tracking (Weights & Biases, MLflow or similar)

Educational Qualification

•     BE / B.Tech / ME / M.Tech in Computer Science

•     BE / B.Tech / ME / M.Tech in any discipline with proven Computer Vision coursework or work

•     MSc / MS in Computer Science, Maths, Statistics or Computer Vision

•     PhD in Computer Vision or Machine Learning: an advantage, not a requirement

•     Reputed Tier 1 university preferred

The role

A working product and a working deployment are two different things. You are the person who closes that gap.

You sit inside the client's head office. You get the deployment live, you get their teams using the dashboards, and you own whether the AI is returning something worth acting on. Every client is different: different languages on the floor, different store noise, different vocabulary for the same product, different CRM, different idea of what a good conversation looks like. The core platform does not change for each of them. You are the layer that makes it fit, and you are the one the client meets.

You are encouraged to spend time in stores. The engineers who do the best work here are the ones who have stood on a shop floor and watched where the pitch and the pipeline actually break. Nobody will make you go. You will also spend real time in the codebase, because you fix what you find rather than filing it.

What you build has a commercial edge to it. A pilot converts when the client sees the result they were promised, and an account grows when a second team inside it sees what is already sitting in their data. Both of those outcomes are yours to deliver, not somebody else's to chase.

How you will work

You design the deployment, you build it, and you own whether it holds up on a Saturday evening in a crowded store. Nobody hands you the plan, and nobody hands you the spec. You write both.

The decisions are yours. Which integration is worth the week, which vertical taxonomy needs building, what ships in the pilot and what waits, and when to tell a client that the thing they are asking for is the wrong thing to build. You go and find out what a client needs before anyone writes a line of code.

You will not be doing it alone. There are founders, AI engineers and product people around you, and they will build alongside you. What nobody will do is tell you what the client needs. That call is yours.

The work compounds if you do it well. What you learn on one deployment becomes a specification, then code, then a pattern the next one starts from. A year in, the deployments you designed should be running without you, and a new client should take a fraction of the time the first one did.

What you will do

Own the deployment end to end. Device provisioning, store connectivity, data flowing, first insight in front of the client. Get from kickoff to something real inside

the pilot window, and know by the halfway mark whether it is in trouble.

Get their HQ using it. A dashboard nobody opens is a failed deployment. Sit with the sales, marketing and L&D teams, show them what is in their own data, and

make sure the people who asked for this are actually looking at it every week.

Make the AI work on their floor. Their languages, their store noise, their product vocabulary. Benchmark transcription and speaker separation on their actual

audio, and fix what fails instead of explaining it away.

Build the vertical. Intent taxonomies, objection maps and prompt libraries for the category you are deployed into. A jewellery floor and an electronics floor do not

share a conversation model.

Wire it into their systems. CRM and POS integrations, so conversation data connects to what actually got sold and the insight can be checked against reality.

Build what the client asks for. Custom reports, dashboards and agents. Ground everything in source conversations and verify it before it ships, because a confident

wrong number costs an account.

Close the pilot. A pilot converts on results, not on effort. Know what the client agreed to judge this on, work backwards from it, and make sure the output in front of

their leadership at the end is the thing they asked for.

Grow the account. The same intelligence is worth something to marketing, L&D and category teams inside the same client. Spot which of them would benefit, show them what is already in their data, and hand a real opening to the account team.

Push it back into the product. Turn one-off client work into something the platform does by default, so the next deployment starts further ahead than this one did.

What we are looking for

Must have

• 0 to 5 years of experience. A consulting internship or an analyst role is the closest match to what this job actually asks for, but we care more about what you can do than where you did it
• Coding ability, ideally Python. Degree, internship, first job or your own projects. What we want to see is something you built that other people actually used
• Excel or Sheets at a real working level. A lot of the first conversation with a client happens in a spreadsheet before it ever happens in a dashboard
• The ability to explain a complicated idea simply. You will be taking AI output to people who do not think about models, and the explanation matters as much as the result
• Comfort at the boundaries. APIs, data pipelines, some frontend, some hardware when a device misbehaves
• An eye for where a deployment turns into more business, and the willingness to raise it yourself
• Heavy hands-on LLM usage. Prompts, evaluations, retrieval, and a clear view on where these tools break
• Fluent English and Hindi. A third Indian language counts for a lot, since the useful conversations happen on store floors and not only in HQ meeting rooms
• The instinct to go and find out what a client needs rather than waiting to be told

Good to have

Speech or audio work. Transcription, diarization, voice activity detection, or

anything that survives noisy real-world recording

Embedded or IoT experience, on ESP32 or similar

SQL and experience building things customers actually look at

Side projects, hackathons or internships where you shipped without a spec

and it worked

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will join the team that creates catalog imagery for sellers at scale, working closely with a Senior Engineer who will train you. You will start on well-defined tasks and grow into writing the prompts and the Python that produce the images.

Who We Are Looking For

• Total experience: 0 to 1 year, internships included

• Python: you can write and debug your own code

• No prior AI or e-commerce experience needed; we will teach you

• Final-year students and recent graduates are welcome to apply

You will be paired with a Senior Engineer and given a structured 90-day ramp. We are hiring for aptitude and attitude, not for a CV.

Skills You Bring

• Python: you can write and debug your own code. This is what we will test, and the only hard requirement.

• Curiosity about AI: you have played with ChatGPT, Claude, Gemini or image generation tools and want to build with them

• Care about detail: you notice when something looks slightly off

Good to have

• Any exposure to image editing, or to Python image libraries such as Pillow or OpenCV; college projects, hackathons or open-source work

What You Will Learn Here

• Prompt engineering for image generation models

• Image manipulation in Python: resizing and interpolation, contrast adjustment, overlaying and joining images

• How a real e-commerce catalog works, and what the marketplaces will and will not accept

Other Relevant Skills

• Communicates clearly in English, written and spoken

• Reliable and organised: you finish what you pick up, and ask for help early

• Willing to do hands-on production work while you learn; the first months mix real output with learning

Educational Qualification

• BE / B.Tech in Computer Science, IT or any engineering discipline

• BCA or MCA

• BSc / MSc in Computer Science, Maths, Statistics or Physics

• Or equivalent practical experience with a portfolio of projects

 The Role 

As a **DevOps Engineer** you'll own the infrastructure and delivery backbone that 

keeps our platform running as we grow. You'll build the CI/CD, cloud infrastructure, and 

observability that let a small, fast-moving team ship confidently — and you'll keep our AI and 

data workloads reliable and affordable at scale. 

This is a hands-on role with real ownership: you won't be maintaining someone else's setup, 

you'll be shaping ours. You'll work closely with the backend, AI/ML, and data teams to make 

deployment boring, incidents rare, and scaling a non-event. --- 

What You'll Own 

**CI/CD & developer experience** 

- Build and maintain fast, reliable CI/CD pipelines so engineers ship multiple times a day with 

confidence. - Make the path from commit to production simple, safe, and repeatable, with sensible 

automated testing, rollbacks, and release controls. 

**Cloud infrastructure & IaC** - Own our cloud infrastructure (AWS/GCP) end to end, managed as code (Terraform or 

similar) — no click-ops. - Design for scale and cost-efficiency as store and conversation volumes grow. 

**Containers & orchestration** - Run our services on containers/Kubernetes: deployments, autoscaling, networking, and 

resource management. - Support the specific needs of AI/ML workloads, including GPU-backed inference and batch 

processing for the speech pipeline. 

**Reliability & observability (SRE)** - Own uptime, performance, and incident response — monitoring, logging, tracing, alerting, 

on-call, and blameless postmortems. - Define and defend SLOs; keep the platform dependable as it scales across clients. 

**Data & pipeline infrastructure** - Support the infrastructure behind large-scale, edge-to-cloud data movement and 

processing (audio ingestion, ASR/AI pipelines, analytics). - Keep data workloads reliable, performant, and cost-aware. 

**Security & compliance** - Bake security into the platform: secrets management, IAM/least-privilege, encryption in 

transit and at rest, network hardening, and vulnerability management. - Support compliance readiness (including India's DPDP Act and enterprise-client security 

requirements) for a product that handles sensitive customer conversations. 

**Cost & scale** - Own cloud cost visibility and optimization; make scaling decisions that balance reliability 

and spend. --- 

What You'll Bring - 6+ years in DevOps, SRE, platform, or infrastructure engineering, running production 

systems at meaningful scale. - Strong hands-on experience with a major cloud provider (**AWS or Azure or GCP**) and 

Infrastructure-as-Code (**Terraform** or equivalent). - Solid experience with **containers and Kubernetes** in production. - Experience building and owning **CI/CD** pipelines (e.g. GitHub Actions, GitLab CI, 

Jenkins, Argo, or similar). 

- Comfort with a scripting/automation language (Python, Go, or Bash) and a strong 

automation-first mindset. - Real experience with **observability** (Prometheus/Grafana, ELK, Datadog, 

OpenTelemetry, or similar) and running incident response / on-call. - A security-conscious approach — secrets, IAM, encryption, and least-privilege as defaults. - Startup temperament: ownership, pragmatism, and a bias to automate and ship. - Based in or willing to relocate to Bangalore, and up for an onsite/hybrid, in-person team. 

Bonus Points - Experience running **ML/AI or GPU workloads** in production (inference serving, batch 

pipelines, model deployment). - Experience with data-intensive infrastructure — streaming/queues (Kafka, SQS), data 

pipelines, or large object/audio storage. - Exposure to **edge devices / IoT fleets**, OTA updates, or high-volume device-to-cloud 

ingestion. - Experience with compliance/security frameworks (SOC 2, ISO 27001, DPDP). - FinOps / cloud cost-optimization experience. - Early-stage startup experience. --- 

Why Join - Own infrastructure that's already live with leading retail brands and growing fast — real 

scale, real impact. - Work across genuinely interesting workloads: speech AI, GPU inference, large-scale data, 

and edge-to-cloud ingestion. - Small team, high ownership, direct line to engineering leadership — your decisions ship. - Build the platform foundation of a category-defining product from an

As a Customer Support Executive / Manager, you will own a set of client accounts and ensure they successfully adopt and use the product.

You will handle client onboarding, product training, reporting, data requests and day-to-day client queries. You will also identify repetitive requests and use AI and automation to make recurring reports and workflows more efficient.

Location: Bangalore / Hybrid

Duration: 6 months

Internship type: Full-time

Stipend: ₹20,000 per month

Potential outcome: Full-time opportunity based on performance

About the Role

We are looking for a hands-on Supabase Engineering Intern to help build and strengthen the backend of our SaaS products.

You will work with Supabase not merely as a hosted database, but as a complete backend platform—including PostgreSQL, authentication, Row-Level Security, storage, database functions, migrations, and application integrations.

This is a full-time, six-month internship suited to someone who has already built projects using Supabase and wants experience working on a real multi-tenant production application.

What You Will Work On

• Design and maintain PostgreSQL tables, relationships, indexes, and constraints.
• Implement secure multi-tenant access using Supabase Row-Level Security policies.
• Integrate Supabase Auth with a Next.js and TypeScript application.
• Build backend workflows using database functions, triggers, RPCs, and Edge Functions.
• Manage schema migrations across development and production environments.
• Work with Supabase Storage and implement secure file-access policies.
• Diagnose slow queries and improve database performance.
• Build reliable application APIs and data-access layers.
• Write seed data, automated tests, and technical documentation.
• Help investigate and resolve production database, authentication, and permission issues.
• Review existing implementations for data leakage, incorrect RLS policies, and security risks.

Required Skills

• Practical experience building at least one project using Supabase.
• Good understanding of SQL and relational database fundamentals.
• Familiarity with PostgreSQL tables, joins, indexes, constraints, and transactions.
• Experience with JavaScript or TypeScript.
• Basic experience with Next.js, React, Node.js, or another modern web framework.
• Understanding of authentication and authorization concepts.
• Ability to use Git and GitHub.
• Strong debugging and problem-solving skills.
• Ability to commit full-time for the complete six-month internship.

Good to Have

• Experience implementing Supabase Row-Level Security policies.
• Understanding of multi-tenant SaaS architecture.
• Experience with Supabase Edge Functions, database functions, triggers, or RPCs.
• Familiarity with PostgreSQL query optimization and EXPLAIN ANALYZE.
• Experience managing Supabase migrations through the CLI.
• Knowledge of REST APIs, webhooks, background jobs, or third-party integrations.
• Experience using AI coding tools such as Cursor, Claude Code, or Codex.
• Contributions to open-source projects or independently deployed applications.

Who Should Apply

You may be a good fit if you:

• Have built and deployed a working Supabase application.
• Enjoy backend engineering, databases, and debugging.
• Can explain why you structured your database and access policies in a particular way.
• Are comfortable learning through documentation and experimentation.
• Take ownership instead of waiting for detailed instructions for every task.
• Want meaningful product-engineering experience rather than a certificate-based internship.

What You Will Learn

• How production-grade multi-tenant SaaS applications are designed.
• Secure database access using PostgreSQL Row-Level Security.
• Development-to-production database migration workflows.
• Authentication, authorization, storage, and backend architecture.
• Performance optimization and production debugging.
• How a startup engineering team ships and operates real products.

Application Process

To apply, please share:

1. Your resume.
2. GitHub profile.
3. Links to one or two relevant projects.
4. A short explanation of how you used Supabase in one project.
5. An example of an RLS policy, database function, or Edge Function you have written.
6. Your current location and availability.
7. Confirmation that you can commit full-time for six months.

Important: Tutorial-only projects will not be sufficient. We are looking for candidates who can demonstrate that they understand the database and security decisions made in their projects.