Graphic Designer

Role & Responsibilities

Responsibilities

• Business: Immerse in operations until you think like an insider.

Rapidly acquire domain expertise through direct observation, translate between business and engineering seamlessly, and mentor engineers in your area on immersion. Influence senior stakeholders effectively, manage complex stakeholder landscapes with competing agendas, and build trust rapidly with new stakeholders.

• Delivery: Lead rapid delivery initiatives across teams in your area, coach on prototype-first approaches, and establish trust through consistent fast delivery. Build complete applications rapidly across any technology stack, select the right tools for each problem, and define clear criteria for prototype-to-production transitions.

• Generative AI: Architect RAG systems for complex use cases across teams, implement advanced techniques (hybrid search, reranking, query expansion), mentor engineers on RAG best practices, and establish RAG standards. Lead evaluation strategy across teams, establishing annotation guidelines, training human-calibrated LLM judges, and building evaluation pipelines that connect tracing to datasets to experiments.

• People: Build high-performing teams across your area, navigate complex interpersonal dynamics, foster psychological safety, and create environments where diverse perspectives are valued. Influence through communication at all levels — from frontline to executive. Handle difficult conversations skilfully and train engineers in your area on effective communication.

• AI-Augmented Development: Optimise AI tool usage across teams in your area, train engineers on AI-augmented and agentic engineering workflows, evaluate new AI development tools, and establish practices that balance AI speed with verification rigour.

• Scale: Design complex multi-component systems end-to-end, evaluate architectural options for large initiatives across teams, guide technical decisions for your area, and mentor engineers on architecture. Create debt reduction strategies across teams, influence roadmap decisions to include debt work, and teach engineers when to accept debt for speed versus when to invest in quality.

• Documentation: Define documentation standards across teams in your area, create documentation systems and templates, train engineers on spec-driven development, and ensure documentation quality across projects. Lead pattern generalization initiatives, defining criteria for when to generalize versus keep custom.

• Reliability: Define reliability standards across teams in your area, drive post-incident improvements systematically, design capacity planning processes, andmentor engineers on SRE practices.

Ideal Candidate

• Strong Staff Software Engineer / FDE profile (full-stack + production GenAI, multi-team technical leadership)
• Mandatory (Experience 1) – Must have 7+ years of relevant professional software engineering experience, with demonstrated full-stack delivery across backend and frontend.
• Mandatory (Experience 2) – Must have deep production experience with Python AND JavaScript/TypeScript, working comfortably across the full stack.
• Mandatory (Experience 3) – Must have 2+ years of experience in generative AI applications developement — LLM integrations, vector databases, RAG systems, and evaluation pipelines
• Mandatory (Experience 4) – Must have strong experience with modern frontend frameworks (Next.js / React) and backend API development.
• Mandatory (Experience 5) – Must have extensive experience with cloud platforms (AWS preferred; Azure/GCP valued), including infrastructure-as-code (CloudFormation / Terraform).
• Mandatory (Experience 6) – Must have working knowledge of multiple database paradigms — relational (PostgreSQL), document, and key-value (Redis) — with ability to select the right storage per problem.
• Mandatory (Experience 7) – Must have strong experience with CI/CD pipelines (e.g. GitHub Actions), containerization, and production deployment strategies.
• Mandatory (Experience 8) – Must have demonstrable fluency with AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) and proven ability to design agentic engineering workflows and train teams on them
• Preferred (Experience) – Advanced RAG techniques — hybrid search, reranking, query expansion — and establishing RAG standards across teams

About the Role

We are looking for a Full Stack AI Engineer who can take an ambiguous problem and turn it into a complete, production-ready AI product.

This is a builder role.

You will work across the entire stack — AI models, agents, backend services, APIs, databases, data pipelines, frontend applications, infrastructure, and production deployment. You should be comfortable deciding what needs to be built, writing the code, deploying it, measuring whether it works, and continuously improving it.

We are not looking for someone who only builds notebooks, trains models, writes prompts, or creates architecture diagrams for another team to implement. We want engineers who ship complete products.

A typical project might involve designing an agentic workflow, building a retrieval pipeline, writing Python APIs, creating a React interface, integrating enterprise data, deploying to Kubernetes, implementing evaluations, and debugging the application in production.

The distance between an idea and working software should be measured in weeks, not quarters.

This role is based in Hyderabad and is 6 days per week in the office.

We are looking for:

1. A person that can smoothly navigate extreme ambiguity
2. Full stack software builder
3. AI depth, must have built a production AI system
4. Excellent top-notch communication and stakeholder management
5. Prioritizes growth over work-life balance

What You'll Do

Build AI Products End-to-End

• Own AI applications from problem definition through architecture, development, deployment, and production operation.
• Translate ambiguous product and business requirements into working software.
• Build across AI/ML, backend services, APIs, databases, frontend interfaces, data pipelines, authentication, infrastructure, and observability.
• Rapidly prototype, test with real users and data, and turn successful ideas into production-grade systems.
• Make pragmatic engineering decisions based on speed, reliability, simplicity, maintainability, and user value.

AI, LLMs & Agents

• Build production applications using commercial and open-source foundation models.
• Design RAG systems, agentic workflows, tool/function calling, structured outputs, memory, human-in-the-loop workflows, and multi-agent systems where appropriate.
• Work with frameworks such as LangGraph, LangChain, Semantic Kernel, LlamaIndex, or equivalent tools.
• Build retrieval systems using embeddings, vector search, BM25, hybrid retrieval, reranking, metadata filtering, and knowledge graphs.
• Design prompt and context engineering strategies for complex workflows.
• Evaluate model choices based on accuracy, latency, reliability, security, and cost.
• Build automated evaluations and regression tests for AI behavior.
• Fine-tune or adapt models when prompting and retrieval are insufficient.

Backend, Frontend & Data

• Build production backend systems primarily in Python using FastAPI, Flask, Django, or similar frameworks.
• Design APIs, asynchronous workflows, background jobs, queues, caching layers, and event-driven systems.
• Work with PostgreSQL, SQL Server, MongoDB, Redis, Snowflake, and other production data stores.
• Build modern applications using React, Next.js, TypeScript, JavaScript, or equivalent frameworks.
• Create interfaces for copilots, conversational AI, workflow automation, analytics, review queues, and operational applications.
• Implement streaming responses, real-time updates, authentication, permissions, and API integrations.
• Build ingestion and transformation pipelines for structured and unstructured enterprise data.
• Work with documents, databases, APIs, event streams, images, logs, and operational datasets.
• Maintain provenance, permissions, metadata, and traceability across enterprise information.

ML & Computer Vision

• Use classical ML or deep learning when it is better suited to the problem than an LLM.
• Build systems involving classification, forecasting, anomaly detection, ranking, recommendations, optimization, or prediction.
• Build computer-vision applications involving detection, classification, segmentation, OCR, tracking, or image/video analysis.
• Work with PyTorch, TensorFlow, Hugging Face, OpenCV, or equivalent tools.
• Understand model development, evaluation, inference, and productionization.

Deploy & Operate What You Build

• Deploy applications across AWS, Azure, GCP, on-premises, hybrid, or edge environments.
• Containerize and operate applications using Docker and Kubernetes.
• Build CI/CD pipelines, automated testing, monitoring, and observability.
• Own reliability, latency, availability, security, evaluation, cost, and scalability.
• Debug failures across application code, AI models, data, infrastructure, and integrations.
• Build retries, fallbacks, rollback mechanisms, and human intervention into critical systems.

Integrate With Enterprise Systems

• Connect AI applications to enterprise platforms, databases, APIs, and operational systems.
• Integrate with systems such as ERP, MES, PLM, CRM, data warehouses, IoT platforms, document repositories, and legacy applications.
• Work within enterprise networking, security, and data-governance constraints.
• Implement authentication, authorization, secrets management, auditability, permissions, and data isolation.
• Build AI systems capable of safely operating on sensitive enterprise data.

What You Bring

• 5+ years of software engineering experience, with meaningful experience building AI/ML-powered products. Exceptional candidates with less experience but strong demonstrated ability will be considered.
• Strong hands-on programming ability in Python.
• Experience building complete production applications rather than isolated models, notebooks, or proofs of concept.
• Strong backend fundamentals including APIs, databases, distributed systems, and application architecture.
• Experience with React, Next.js, TypeScript, JavaScript, or equivalent frontend technologies.
• Strong understanding of LLMs, RAG, agents, tool calling, embeddings, vector search, prompt/context engineering, AI evaluation, and ML fundamentals.
• Experience with SQL and production databases.
• Experience with at least one major cloud platform: AWS, Azure, or GCP.
• Experience with Docker, Kubernetes, or equivalent production infrastructure.
• Understanding of production AI concerns including reliability, latency, security, observability, evaluation, and cost.
• Strong debugging skills across the full stack.
• Ability to independently turn loosely defined requirements into working software.
• Strong product judgment, high agency, technical curiosity, and a bias toward shipping.
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent demonstrated experience.

Preferred / Top-Candidate Signals

• You've independently shipped an AI application from database → backend → AI → frontend → production.
• You've built production RAG or agentic systems, not just demos.
• You've used LangGraph, LangChain, Semantic Kernel, LlamaIndex, or similar frameworks.
• You understand when not to use an LLM or agent.
• You've worked with hybrid retrieval, reranking, vector search, or knowledge graphs.
• You've built with both commercial and open-source models.
• You've deployed ML or computer-vision systems into production.
• You have experience with React/Next.js + Python/FastAPI or a comparable modern stack.
• You have experience with Kubernetes, cloud infrastructure, and production observability.
• You have integrated software with complex enterprise or industrial systems.
• Experience in manufacturing, industrial, supply chain, logistics, engineering, energy, aerospace, automotive, or other physical-world environments is a strong plus.
• You've built meaningful side projects, open-source software, startups, or substantial systems outside your assigned responsibilities.
• You have a history of turning vague ideas into shipped products.

What Makes Someone Exceptional

The strongest engineers in this role combine three abilities:

AI Engineering — Choose the right model, retrieval approach, agent architecture, evaluation method, or ML technique.

Software Engineering — Build everything around the intelligence: frontend, backend, data, APIs, infrastructure, security, integrations, and deployment.

Product Judgment — Understand what actually needs to be built and rapidly turn it into something users can use.

A typical week might involve designing an agent workflow, writing FastAPI services, building a React interface, creating a retrieval pipeline, connecting enterprise data, deploying to Kubernetes, implementing evaluations, and debugging real-world behavior.

You should not need five different teams to turn an idea into a working product. You should be able to build.

Why This Role

• Build entire products, not isolated models or prototypes.
• Own the full stack: AI, backend, frontend, data, infrastructure, and deployment.
• Ship quickly: move from idea to working software in weeks.
• Work across modern AI: LLMs, agents, RAG, ML, computer vision, optimization, and enterprise data.
• Solve real-world problems: build software used in complex operational environments.
• See your work in production: own the path from first commit to real users.
• Compensation is flexible for exceptional candidates. 

• Program Delivery: Own end-to-end delivery across multiple parallel workstreams — plans, dependencies, risks, budgets, and outcomes.
• Stakeholder Management: Run steering committees, executive readouts, and weekly governance with VP/Director-level leadership.
• Vendor Management: Coordinate third-party vendors, hold them to commitments, and escalate with judgment.
• Communication: Deliver crisp status reporting, early risk flagging, and zero-surprise escalation management.
• Alignment: Align engineering leads, architects, and client teams on priorities; unblock delivery without micromanaging.
• US Overlap: Maintain US business-hours overlap for daily client leadership interaction and real-time decision-making.

Must-Have Skills

• Technology Delivery: 12+ years in technology delivery, with 6+ years running multi-team, multi-million-dollar programs.
• Stakeholder Expertise: Track record of owning C-level and VP-level client relationships, including expectation resets.
• Executive Communication: Written and verbal communication at executive standard; presence in leadership rooms.
• Vendor Management: Contracts, SLAs, and performance management of third parties.
• Consulting Background: Comfortable representing the firm to the client.
• US Hours: Proven ability to work US-overlapping hours consistently.
• Program Governance: Strong command of RAID, financials, resourcing, and change control (PMP/SAFe/PgMP a plus).

Good-to-Have Skills

• Experience with DevOps Programs, Observability, or Platform Engineering.
• Expertise in Onsite-Offshore Delivery.
• Experience Recovering Troubled Programs or taking over from underperforming vendors.

Perks & Benefits

·        Competitive salary + performance bonuses

·        Remote-first culture with flexible hours

·        Health, dental, and vision insurance

·        Annual learning & certification budget

·        Work with a passionate, mission-driven team

DevOps Engineer

We are looking for a hands-on DevOps Engineer to manage and scale our cloud infrastructure, Kubernetes-based microservice deployments, monitoring systems, and data engineering infrastructure.

The person will be responsible for building reliable, secure, scalable, and cost-efficient infrastructure using automation-first practices. This role is important for supporting a high-growth B2C platform where availability, deployment velocity, observability, security, and cost efficiency are critical.

Key Responsibilities

• Manage and automate cloud infrastructure using Terraform.
• Deploy, manage, and troubleshoot microservices on Kubernetes.
• Build and maintain CI/CD pipelines to ensure reliable, controlled deployments.
• Implement safe release practices, including rolling deployments, rollback, and zero-downtime deployments.
• Manage monitoring, logging, alerting, dashboards, and production runbooks.
• Support incident response, production debugging, RCA, and preventive action closure.
• Ensure infrastructure is scalable, secure, highly available, and cost-optimised.
• Support data engineering infrastructure, including ClickHouse, PeerDB, Airflow, Kafka, and related platform components.
• Maintain infra-level security controls, backups, disaster recovery, and access governance.

Required Skills

• Strong experience with Terraform, Infrastructure as Code, and AWS.
• Strong experience with Kubernetes, Docker, Helm, ingress, and autoscaling.
• Experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, ArgoCD, or similar.
• Experience with monitoring and observability tools such as Prometheus, Grafana, ELK/OpenSearch, New Relic, or similar.
• Good understanding of cloud networking, DNS, load balancers, VPC/VPN, SSL/TLS, firewalls, and WAF.
• Experience with Linux administration, shell scripting, and automation.
• Understanding of cloud security, IAM, secrets management, and access governance.
• Exposure to databases, queues, caches, and data infrastructure tools such as ClickHouse, PeerDB, Airflow, Kafka, or similar.
• Strong debugging and problem-solving skills during production incidents.
• Ability to work closely with engineering teams to improve deployment, monitoring, cost, and reliability. 

Required Skills:

· Experience in implementing Object-Oriented Python, Django.

· Good understanding on Django ORM.

· Experienced in interfacing with *third party API’s using REST

· Worked with varieties of Relational Databases (RDBMS) like SQLite, MySQL, PostgreSQL.

· Experience in Version Control with Git and Bitbucket.

· Experience with JIRA the development progress and tracking deadlines of the project

Overview

Apiwiz (Itorix Inc) is looking for software engineers to join our team, grow with us, introduce us to new ideas and develop products that empower our users. Every day, you’ll work with team members across disciplines developing products for Apiwiz (Itorix Inc). You’ll interact daily with our product managers to understand our domain and create technical solutions that push us forward. We want to work with other engineers who bring knowledge and excitement about our opportunities.

You will impact major features and new product decisions as part of our remarkably high performing, collaborative team of engineers who thrive on the business impact of their work. With strong team support and significant freedom and self direction, you will experience the wealth of interesting, challenging problems that only a high growth startup can provide.

Roles & Responsibilities

• Build, configure, and manage cloud compute and data storage infrastructure for multiple instances of AWS and Google Cloud Platform.
• Manage VPCs, security groups, and user access to our various public cloud systems and services.
• Develop processes and procedures for using cloud-based infrastructures, including, access key rotation, disaster recovery, and building new services.
• Help the business control costs by categorizing and tagging assets running in the cloud.
• Develop scripts and workflows to manage cloud computing systems
• Provide oversight on log aggregation and application performance monitoring surrounding our production environments.

What we’re looking for

• 2-3 years of experience in the provision, configuring, administrating, automating, monitoring, and supporting enterprise Cloud services
• Strong experience in designing, building, maintaining and securing AWS resources for high-availability and production level systems and services
• Familiar with Cloud concepts with practical hands-on experience on any Cloud Platform.
• Hands-on experience with AWS services like Elastic Compute Cloud (EC2), Elastic Load-balancers, S3, Elastic File system, VPC, Route53, and IAM.
• Providing 24/7 support for the application and Infrastructure support
• Prior experience using infrastructure as a code software tool like Terraform.
• Knowledge in software provisioning, configuration management, and application-deployment tools like Ansible.
• Working knowledge of container technologies like Docker & Kubernetes cluster operations.
• Familiarity with software automation tools Git, Jenkins, Code Pipeline, SonarQube

Automation Engineer

About Tazapay Tazapay is founded by 3 seasoned professionals with extensive experience in payments, ecommerce, technology, cross border trade and high growth technology businesses. The company is financially backed by the likes of Sequoia Capital, Saison Capital, RTP Global etc. We https://tazapay.com/ are a Singapore-based b2b and payments startup enabling small and medium-sized businesses (SMBs) to conduct cross-border commerce securely.

About the Automation Engineer role Tazapay is looking for an Automation Engineer who will assist testing across a suite of Mobile and Web Applications, ensuring all errors are identified and corrected before release to customers. This role will work to ensure high quality is central to development, and work closely with developers to give them a testing perspective from scoping acceptance criteria through to release.

Responsibilities (not exhaustive)

• Work in a cutting-edge agile software development environment as an Automation Engineer ensuring the quality of our Tazapay Platform

• Gain technical and functional understanding of our product architecture and become part of our ongoing feature and enhancement development effort

• Gain Manual testing expertise to write test cases/plans, execute test cases, log bugs in Defect tracking tool, verify bugs.

• Communicate with product managers in creating acceptance tests as part of the requirements, and execute those tests manually, as well as automate them

• Manage the test case inventory and work with other members of the Software Testing team

• Provide guidance when needed to other members of Software Testing team and Product Support

• Work proactively with members of an Agile Software Development and Software Testing team to find and fix defects in our product

• Produce and maintain test coverage reports for functional and automation coverage

• Communicate critical issues and status updates in a timely manner to the team and other stakeholders.

• Ensure all reporting is succinct yet includes all necessary details for replication and timely resolution.

The Ideal Candidate Education

• Bachelor’s degree in Computer Science, Information Technology or related field.

• Understanding of the software development life cycle as well as the business approach for the product. Experience

• Minimum of 3 years of working experience as an automation engineer Abilities and Traits

• Ability to work under pressure and meet deadlines

• Ability to provide exceptional attention to details of the product.

• Ability to focus for extended periods of repetitious activity.

• Ability to think ahead and anticipate problems, issues and solutions

• Work well as a team player and help the team members to resolve issues

• Be committed to quality and be structured in approach

• Excellent and demonstrable concept formulation, logical and analytical skills

• Excellent planning, organizational, and prioritization skills Location

Location: work from home

Experience : 3 yrs plus

Package: uto 20LPA

Customer service and care of the customers

• By entering customer data in to the data base
• By updating customer information
• By handling customer telephone calls and making required activities
• By actively preventing terminations of contracts and reduce of purchased services
• By handling customer feedback and making required activities
• By making customer satisfaction surveys
• By follow up of first delivery to new customer
• By follow up of contract termination and final invoicing
• By making invoicing to customers
• By printing of delivery lists
• By archive of customer documents