We are seeking a skilled and motivated AWS Cloud Engineer to manage and optimize our cloud infrastructure. You will be responsible for designing, implementing, and maintaining scalable, secure, and cost-effective AWS environments that support our fintech products and services. Key Responsibilities: Design, deploy, and maintain cloud infrastructure on AWS. Automate provisioning, configuration, and scaling using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation. Monitor system performance, troubleshoot issues, and optimize cloud resources for performance and cost. Implement security best practices including IAM roles, security groups, and encryption. Collaborate with development, QA, and DevOps teams to support CI/CD pipelines. Ensure high availability, backup, and disaster recovery plans are in place and tested. Maintain compliance with security, governance, and regulatory standards. Key Skills: Deep knowledge of Amazon Web Services (AWS) : EC2, S3, RDS, Lambda, VPC, CloudWatch, etc. Experience with Infrastructure as Code : Terraform or AWS CloudFormation. Strong scripting skills (Bash, Python, etc.). Knowledge of CI/CD tools : Jenkins, GitHub Actions, GitLab CI. Experience with monitoring/logging tools: CloudWatch, ELK Stack, Prometheus, Grafana. Understanding of cloud security best practices and networking concepts . Familiarity with containerization (Docker) and orchestration (Kubernetes optional). Experience with Linux-based server environments.

We are looking for a meticulous and proactive QA Engineer (Automation & Manual) to join our technology team. You will be responsible for ensuring the quality and reliability of our web and mobile applications through rigorous testing processes. This role requires hands-on experience in both manual testing and automated testing frameworks . Key Responsibilities: Design, develop, and execute manual and automated test cases based on product requirements. Identify, document, and track bugs through to resolution using issue tracking tools. Collaborate with developers, product managers, and UX designers to understand features and technical implementations. Set up and maintain automation frameworks and tools (Selenium, Cypress, etc.). Perform regression, performance, and smoke testing to ensure system stability. Continuously improve test processes and contribute to QA best practices. Participate in release planning, code reviews, and sprint activities. Key Skills: Strong understanding of QA methodologies, tools, and processes. Hands-on experience with manual testing across web and mobile platforms. Proficiency in automation tools like Selenium, Cypress, Playwright, or Appium. Experience writing test scripts in JavaScript, Java, or Python. Familiarity with CI/CD tools such as Jenkins, GitLab CI, or GitHub Actions. Strong knowledge of RESTful APIs and tools like Postman. Experience with bug tracking systems (e.g., Jira, Bugzilla). Ability to write clear, concise, and comprehensive test plans and test cases. Good analytical and problem-solving skills. Experience in fintech or trading applications is a plus .

*Design and deploy ML models* to score trader performance, risk exposure, and funding eligibility in real-time. Build AI systems to: * Auto-journal trades and classify PnL events * Detect trader manipulation, bots, or copy trading behavior * Identify high-potential traders early (funding prioritization) Use LLMs and NLP to create smart summaries, trade notes, and insights from user behavior and support queries. Own the *AI infrastructure*, from experimentation to production (Docker, APIs, CI/CD). Collaborate closely with our backend, frontend, and R\&D teams to bring AI features into the trader dashboards and CRM. Constantly explore new AI tools, algorithms, and research relevant to fintech and trading ecosystems. *Ideal Candidate Profile* *4+ years of hands-on experience* in building and deploying ML/AI models in production environments. Solid foundation in *machine learning, deep learning, and statistics*. Expertise with tools like *Python, PyTorch/TensorFlow, Scikit-learn, XGBoost, HuggingFace*, etc. Strong data pipeline and API experience (Airflow, FastAPI/Flask, Pandas, SQL, MongoDB). Comfortable with deploying ML models at scale using *Docker, MLflow, AWS/GCP*. Bonus: Experience in *fintech, trading platforms, fraud detection, or real-time data systems*. Bonus++: Knowledge of reinforcement learning, anomaly detection, or building recommender systems. --- What Youll Build (Example Projects) *Traderscore AI*: A real-time rating engine to predict trader potential and risk based on behavior patterns. *AnomalyBot*: A system that flags suspicious trades and prevents fraud before payout. *Smart Journal*: NLP-powered auto trade logs with emotional sentiment tracking and decision reviews. *TopTraderRadar*: Predictive analytics dashboard to identify breakout talent globally.

Familiarity with OpenAI, Gemini, Bing AI, Perplexity This role involves strategic content structuring, citations, prompt testing, nd organic AI discoverability techniques to ensure our business appears in AI responses, summaries, and recommendations.

Primary Goal: Ensure your platform (e.g. FundedFirm.) appears in ChatGPT answers, Bing Copilot, Google Gemini, Perplexity, etc., when users search for: • Best prop trading firm” • “Instant funding for Forex” • “How does WinProFX work” • “Top Forex brokers in UAE” • “What is FundedFirm” Key Skill Set to Look For: 1. LLM SEO Strategy • Deep understanding of how LLMs ingest and process data (from websites, structured content, citations, etc.) • Knowledge of vector databases, semantic search, and AI retrieval models (RAG) 2. Citation & Mentions Management • Expertise in getting your brand cited in reliable sources like: • Wikipedia (if notable) • Product Hunt / G2 / Capterra • Medium, Quora, Reddit • News websites and PR outlets 3. Structured Data & Schema Markup • Can implement and audit schema.org, JSON-LD, and structured knowledge graphs • Understands how to tag FAQs, reviews, and organization details 4. AI Prompt Engineering (optional but valuable) • Can generate effective prompts that replicate what a user might ask ChatGPT or Gemini to test your discoverability 5. Content & Brand Integration • Works closely with your content/PR/SEO teams to push content that is: • Fact-based • Well-cited • Present across multiple web assets 6. Web & API Visibility • Knows how to expose brand data via APIs or machine-readable content (e.g. RSS feeds, public datasets) 7. Reputation Engineering • Can handle brand narrative through Trustpilot reviews, Glassdoor presence, and LinkedIn knowledge panels Job Description Summary: We’re hiring an LLM SEO & ChatGPT Optimization Specialist responsible for improving our brand’s visibility within AI-powered platforms like ChatGPT, Perplexity, Bard (Gemini), and other LLM-based search tools. This role involves strategic content structuring, citations, prompt testing, and organic AI discoverability techniques to ensure our business appears in AI responses, summaries, and recommendations. Expected Deliverables: • Baseline Audit: Where does the brand currently rank in LLMs? • Citation Plan: 20+ sources where brand mentions can be added • Structured Content: JSON-LD schemas, FAQs, product pages • Visibility Dashboard: Monthly reporting of LLM discoverability • Prompt Testing: Test 50+ prompts monthly and document performance • Page Optimization: Optimize 10+ pages for LLM crawling monthly Ideal Background: • Past experience in SEO + technical SEO • Worked on AI/ML products or conversational UX projects • Familiarity with OpenAI, Gemini, Bing AI, Perplexity • Bonus: Published content about LLMs, SEO, or AI knowledge integration