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1-  Product uploading, Product Listing, Catalogue Management, Product Searching.

2- Candidate should have good knowledge of MS-Excel.

3- Candidate should have good typing speed.

4- Candidate should be proactive in approach

5- Basic knowledge of photo edit is required

Summary

We are looking for an exceptional AI-native full-stack product engineer who can take product specs, prototypes, or PRDs and turn them into clean, tested, QA’d, merge-ready branches.

We are building a fast-moving software company and currently use AI coding tools heavily in our development workflow. Right now, the delivery process is: prototype review, spec clarification, implementation planning, AI-assisted coding, QA, checking what was actually built, and preparing features for final merge.

We want to have this entire workflow managed by the right engineer.

Your job will be to take a feature brief, PRD, prototype, or Loom walkthrough and own the process from:

Input → clarification → implementation plan → AI-assisted build → self-review → QA → PR → merge-ready branch.

We are looking for someone who can take ownership, not create more management work.

What You’ll Do

You will be responsible for:

• Reviewing PRDs, prototypes, tickets, or Loom walkthroughs
• Asking clear, batched clarification questions
• Creating concise implementation plans before coding
• Using AI coding tools to accelerate development
• Reviewing and improving AI-generated code
• Building full-stack product features
• Writing or updating tests where appropriate
• Running QA before handing work over
• Creating clean, reviewable pull requests
• Documenting what was built, what changed, and what risks remain
• Preparing branches that are actually ready to merge
• Communicating clearly and asynchronously

What “Done” Means

A feature is not done when the code is written. A feature is done when:

• The implementation matches the PRD/prototype
• Acceptance criteria are completed
• Code is clean and understandable
• Build/lint/type checks pass
• Relevant tests are added or updated
• Manual QA has been completed
• Screenshots or Loom walkthrough are provided when relevant
• PR description is clear
• Risks, assumptions, and deviations are documented
• The branch is ready for review/merge

Ideal Candidate

You are probably a strong fit if you are:

• An experienced full-stack engineer with strong product judgment
• Experienced with AI coding tools such as Claude Code
• Comfortable taking a vague product idea and turning it into a working feature
• Able to review AI-generated code critically
• Strong with GitHub PR workflows
• Detail-oriented with good QA discipline
• Clear in written communication
• Comfortable working in a fast-moving startup environment
• Able to work independently without needing constant hand-holding
• Good at asking the right questions early

You Are Not a Fit, please do not apply if:

• You blindly trust AI-generated code
• You need every task broken down into tiny instructions
• You say “done” without testing your work
• You create large messy PRs
• You cannot explain your own code
• You are only comfortable with narrow frontend or backend tasks
• You are not comfortable with ambiguity
• You dislike writing implementation plans or QA notes
• You require constant synchronous direction

Required Skills

Please include your experience with:

• Full-stack web development
• Frontend frameworks
• Backend/API development
• Git/GitHub
• Pull request workflow
• Testing and QA
• AI-assisted coding tools
• Debugging and self-review
• Product-focused software development
• Nice to Have
• Startup experience
• Experience as a founding engineer or product engineer
• Strong UX/product taste
• Experience with automated testing
• Experience with CI/CD
• Experience with preview deployments
• Experience turning Figma/prototypes into production features
• Experience working directly with founders

How We Work

We will usually provide one or more of:

• PRD
• Prototype
• Figma
• Loom walkthrough
• Linear/Jira/Notion ticket
• Product brief
• Screenshots
• Existing codebase

You will be expected to return:

• Clarifying questions, if needed
• Implementation plan
• Branch/PR
• QA notes
• Test results
• Screenshots/Loom if relevant
• Clear summary of what was built
• Any risks, tradeoffs, or unresolved questions

Trial Task

We will start with a paid trial task.

The trial will involve a small real feature or product improvement. You will be asked to:

• Review the brief/prototype
• Write a short implementation plan
• Build the feature
• QA it
• Open a clean PR
• Provide a Loom or written walkthrough
• Document assumptions, risks, and anything not completed

We are not just evaluating whether the code works. We are evaluating whether you can own the delivery process end-to-end and how well you leverage AI.

What Success Looks Like

Success means the team can give you a PRD or prototype and receive back a merge-ready branch without needing to manage every step.

Great output looks like:

“I reviewed the feature brief, found two ambiguities, made reasonable assumptions, implemented the core flow, added tests, QA’d happy path and edge cases, documented one tradeoff, and the branch is ready for review. Management's input is only needed on this one product decision.”

Preference will be given to candidates who can work with high ownership and potentially become a long-term product engineering partner.

To Apply, please consider answering the following questions:

• What AI coding tools do you use regularly, and how do you use them?
• Walk us through your process for turning a PRD or prototype into a merge-ready branch.
• How do you QA your own work before asking for review?
• What are the biggest risks of AI-generated code?
• Share an example of a PR, project, or product feature you built that you are proud of.
• What stack are you strongest in?
• Are you comfortable doing a paid trial task?
• What does “merge-ready” mean to you?

Generative AI System Design

• Architect and implement end-to-end LLM-powered applications
• Build scalable RAG pipelines (chunking, embeddings, hybrid search, reranking)
• Design and implement agent-based workflows (tool calling, multi-step reasoning, orchestration)
• Integrate LLM APIs such as OpenAI and Anthropic, along with open-source models
• Implement structured output validation, grounding strategies, and hallucination mitigation
• Optimize inference cost, latency, and token efficiency
• Design evaluation pipelines for performance, accuracy, and safety

2️⃣ Backend & Microservices Engineering

• Design scalable backend systems using Python
• Build REST and async APIs using FastAPI / Django
• Architect and implement microservices with clear service boundaries
• Implement service-to-service communication (REST, gRPC, event-driven messaging)
• Work with message brokers (Kafka / RabbitMQ)
• Optimize database performance (PostgreSQL, MongoDB)
• Implement caching strategies (Redis)
• Build observability: logging, monitoring, distributed tracing

3️⃣ Cloud-Native Architecture & DevOps

• Design and deploy containerized services using Docker
• Orchestrate services using Kubernetes
• Implement CI/CD pipelines
• Ensure system scalability, resilience, and fault tolerance
• Apply distributed systems principles:
• Circuit breakers
• API gateway patterns
• Load balancing
• Horizontal scaling
• Saga patterns
• Zero-downtime deployments

Key Responsibilities

Design and develop facade concepts, details, and technical drawings.

Collaborate with architects, engineers, and contractors to ensure design integration.

Prepare 2D/3D models, renderings, and specifications for facade systems.

Research and recommend materials, finishes, and sustainable technologies.

Review shop drawings, prototypes, and mock-ups for compliance with design intent.

Provide technical support during tendering, procurement, and construction phases.

🛠️ Skills & Competencies

Proficiency in AutoCAD, Revit.

Strong knowledge of facade materials (glass, aluminum, stone, composites).

Understanding of building physics and envelope performance.

BE / Diploma /Mech / Civil

Job Description:

Role: Data Scientist

Responsibilities:

 Lead data science and machine learning projects, contributing to model development, optimization and evaluation. 

 Perform data cleaning, feature engineering, and exploratory data analysis.  

Translate business requirements into technical solutions, document and communicate project progress, manage non-technical stakeholders.

Collaborate with other DS and engineers to deliver projects.

Technical Skills – Must have:

Experience in and understanding of the natural language processing (NLP) and large language model (LLM) landscape.

Proficiency with Python for data analysis, supervised & unsupervised learning ML tasks.

Ability to translate complex machine learning problem statements into specific deliverables and requirements.

Should have worked with major cloud platforms such as AWS, Azure or GCP.

Working knowledge of SQL and no-SQL databases.

Ability to create data and ML pipelines for more efficient and repeatable data science projects using MLOps principles.

Keep abreast with new tools, algorithms and techniques in machine learning and works to implement them in the organization.

Strong understanding of evaluation and monitoring metrics for machine learning projects.

Position- Appian Developer

Experience- 8 to 12 years

Location- Onsite

Appian Skills:

Process Modeling and Workflow Automation: Candidates must showcase their understanding of Appian’s Process Modeler and demonstrate the ability to optimize execution paths and manage exceptions.

Data Integration and Management Expertise: This skill ensures that candidates can create secure API integrations and manage external data sources, crucial for maintaining data integrity and connectivity.

Scripting and Expression Rule Development: Evaluates the candidate’s capability to write expressions and scripts that extend application functionality.

Security and Access Control Management: Assess the ability to configure security roles and access permissions, ensuring that applications comply with organizational policies and protect sensitive data.

Appian Deployment and Performance Optimization: Ensures that applications are deployed efficiently and perform optimally. These skills are essential for designing, implementing, and managing scalable and efficient applications using Appian's low-code platform.

Software Engineer

Opportunity in this role / Why should you join us?

- You are your own master.  We don't preach you, we just allow you to Innovate, Iterate, Learn and Adapt

- Work with one of India's leading fintech unicorn company

- ESOPs in the company

Requirements

• 3+ years and above exposure in the art of writing codes and solving problems for large scale
• Extensive and expert programming experience in any one programming language (strong OOPS skills preferred). Deep experience in a java programming language
• A solid foundation in computer science, with strong competencies in data structures, algorithms, and software design.
• Have a penchant for solving complex and interesting problems.
• BE/BTech, MTech in Computer Science or related technical discipline (or equivalent).
• Excellent coding skills – should be able to convert the design into code fluently Good skills to write unit & integration tests with reasonable coverage of code & interfaces - TDD is a plus
• Experience in building highly scalable business applications, which involve implementing large complex business flows and dealing with the huge amount of data. Experience with

multi-threading and concurrency programming

• Ability to switch between the technologies and learn new skills on the go.

Roles and Responsibilities

• Drive discussions to create/improve the product, process and technology
• Build abstractions and contracts with separation of concerns for a larger scope
• Rapid prototyping
• Robust and scalable web-based applications
• Do high-level design with guidance; Functional modelling, break-down of a module
• Thinking platforms & reuse, the open-source contribution will be a plus
• Do incremental changes to architecture: impact analysis of the same
• Do performance tuning and improvements in large scale distributed systems
• Mentor young minds and foster team spirit.