Key Responsibilities Develop modern, responsive user interfaces using React , Next.js , and TypeScript . Collaborate with cross-functional teams to turn UI/UX designs and product requirements into functional, scalable components. Bring pixel-perfect designs to life with smooth animations and intuitive interactions. Implement and manage application state using tools like Redux , Zustand , Recoil , or Context API . Optimize components for maximum performance across devices and browsers. Ensure code quality, maintainability, and scalability using best practices and modern development standards. Write clean, reusable, and well-documented code. Requirements Required Skills Proficiency in TypeScript and modern JavaScript (ES6+) . Experience with React , Next.js , and frontend build tools. Strong understanding of state management libraries (Redux, Zustand, Recoil, or Context API). Hands-on experience with RESTful APIs and asynchronous data handling. Familiarity with testing frameworks like Jest and React Testing Library . Solid knowledge of HTML , CSS , and responsive design principles. Experience with CSS frameworks like Tailwind CSS , SASS , or similar. Show more Show less

. Role Overview: We are seeking a motivated Junior AI Testing Engineer to join our team. In this role, you will support the testing of AI models and pipelines, with a special focus on data ingestion into knowledge graphs and knowledge graph administration. You will collaborate with data scientists, engineers, and product teams to ensure the quality, reliability, and performance of our AI-driven solutions. Key Responsibilities: AI Model & Pipeline Testing: Design and execute test cases for AI models and data pipelines, ensuring accuracy, stability, and fairness Knowledge Graph Ingestion: Support the development and testing of Python scripts for data extraction, transformation, and loading (ETL) into enterprise knowledge graphs Knowledge Graph Administration: Assist in maintaining, monitoring, and troubleshooting knowledge graph environments (e.g., Neo4j, RDF stores), including user access and data integrity. Test Automation: Develop and maintain basic automation scripts (preferably in Python) to streamline testing processes for AI functionalities Data Quality Assurance: Evaluate and validate the quality of input and output data for AI models, reporting and documenting issues as needed Bug Reporting & Documentation: Identify, document, and communicate bugs or issues discovered during testing. Maintain clear testing documentation and reports. Collaboration: Work closely with knowledge graph engineers, data scientists, and product managers to understand requirements and deliver robust solutions. Requirements Requirements: Education: Bachelor’s degree in Computer Science, Information Technology, or related field. Experience: ideally experience in software/AI testing, data engineering, or a similar technical role. Technical Skills: Proficient in Python (must have) Experience with test case design, execution, and bug reporting Exposure to knowledge graph technologies (e.g., Neo4j, RDF, SPARQL) and data ingestion/ETL processes Analytical & Problem-Solving Skills: Strong attention to detail, ability to analyze data and systems, and troubleshoot issues Communication: Clear verbal and written communication skills for documentation and collaboration. Preferred Qualifications: Experience with graph query languages (e.g., Cypher, SPARQL) Exposure to cloud platforms (AWS, Azure, GCP) and CI/CD workflows Familiarity with data quality and governance practices. Show more Show less

About the Open Position Join us as Cloud Engineer at Dailoqa , where you will be responsible for operationalizing cutting-edge machine learning and generative AI solutions, ensuring scalable, secure, and efficient deployment across infrastructure. You will work closely with data scientists, ML engineers, and business stakeholders to build and maintain robust MLOps pipelines, enabling rapid experimentation and reliable production implementation of AI models, including LLMs and real-time analytics systems. To be successful as Cloud Engineer you should have experience with: Cloud sourcing, networks, VMs, performance, scaling, availability, storage, security, access management Deep expertise in one or more cloud platforms: AWS, Azure, GCP Strong experience in containerization and orchestration (Docker, Kubernetes, Helm) Familiarity with CI/CD tools: GitHub Actions, Jenkins, Azure DevOps, ArgoCD, etc. Proficiency in scripting languages (Python, Bash, PowerShell) Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML Strong understanding of DevOps principles applied to ML workflows. Key Responsibilities may include: · Design and implement scalable, cost-optimized, and secure infrastructure for AI-driven platforms. · Implement infrastructure as code using tools like Terraform, ARM, or Cloud Formation. · Automate infrastructure provisioning, CI/CD pipelines, and model deployment workflows. · Ensure version control, repeatability, and compliance across all infrastructure components. · Set up monitoring, logging, and alerting frameworks using tools like Prometheus, Grafana, ELK, or Azure Monitor. · Optimize performance and resource utilization of AI workloads including GPU-based training/inference Experience with Snowflake, Databricks for collaborative ML development and scalable data processing. Understanding model interpretability, responsible AI, and governance. Contributions to open-source MLOps tools or communities. Strong leadership, communication, and cross-functional collaboration skills. Knowledge of data privacy, model governance, and regulatory compliance in AI systems. Exposure to LangChain, Vector DBs (e. g. , FAISS, Pinecone), and retrieval-augmented generation (RAG) pipelines. Show more Show less

Role Overview As a Scrum Master at Dailoqa, you’ll bridge the gap between agile practices and the unique demands of AI/ML-driven product development. You’ll coach cross-functional teams of software engineers, data scientists, and ML engineers to deliver high-impact solutions while fostering a culture of collaboration, experimentation, and continuous improvement. Key Responsibilities Agile Facilitation & Coaching Facilitate all Scrum ceremonies (sprint planning, daily stand-ups, reviews, retrospectives) with a focus on outcomes, not just outputs. Coach team members (software engineers, AI/ML engineers, data scientists) on Agile principles, ensuring adherence to Scrum frameworks while adapting practices to AI/ML workflows. Sprint & Workflow Management Manage hybrid sprints that include software development, data science research, and ML model training/deployment. Maintain Agile boards (Jira, Azure DevOps) to reflect real-time progress, ensuring transparency for stakeholders. Monitor sprint velocity, burndown charts, and cycle times, using metrics to identify bottlenecks and improve predictability. AI/ML-Specific Agile Leadership Adapt Agile practices to AI/ML challenges: Experimentation: Advocate for “spikes” to validate hypotheses or data pipelines. Uncertainty: Help teams embrace iterative learning and fail-fast approaches. Cross-functional collaboration: Resolve dependencies between data engineers, MLops, and product teams. Continuous Improvement Lead retrospectives focused on both technical (e.g., model accuracy, pipeline efficiency) and process improvements. Drive adoption of Agile engineering practices (CI/CD, test automation) tailored to AI/ML workflows. Qualifications Must-Have 5–8 years as a Scrum Master, with 2+ years supporting AI/ML or data science teams . Deep understanding of Agile frameworks (Scrum, Kanban) and tools (Jira, Azure DevOps). Proven ability to coach teams through AI/ML-specific challenges: Model lifecycle management (training, validation, deployment). Balancing research-heavy work with delivery timelines. Managing data dependencies and computational resource constraints. Certifications: CSM, PSM, or equivalent. • Strong understanding of Agile frameworks (Scrum, Kanban) and SDLC principles • Experience working with cross-functional teams including data scientists and AI engineers • Exposure to AI/ML product development cycles, including research-to-production workflows • Familiarity with AI project elements such as model training, data labeling, GenAI experimentation, or MLOps Show more Show less

Role Overview As a Test Automation Lead at Dailoqa, you’ll architect and implement robust testing frameworks for both software and AI/ML systems. You’ll bridge the gap between traditional QA and AI-specific validation, ensuring seamless integration of automated testing into CI/CD pipelines while addressing unique challenges like model accuracy, GenAI output validation, and ethical AI compliance. Key Responsibilities Test Automation Strategy & Framework Design Design and implement scalable test automation frameworks for frontend (UI/UX) , backend APIs , and AI/ML model-serving endpoints using tools like Selenium, Playwright, Postman, or custom Python/Java solutions. Build GenAI-specific test suites for validating prompt outputs, LLM-based chat interfaces, RAG systems, and vector search accuracy. Develop performance testing strategies for AI pipelines (e.g., model inference latency, resource utilization). Continuous Testing & CI/CD Integration Establish and maintain continuous testing pipelines integrated with GitHub Actions, Jenkins, or GitLab CI/CD. Implement shift-left testing by embedding automated checks into development workflows (e.g., unit tests, contract testing). AI/ML Model Validation Collaborate with data scientists to test AI/ML models for accuracy , fairness , stability , and bias mitigation using tools like TensorFlow Model Analysis or MLflow. Validate model drift and retraining pipelines to ensure consistent performance in production. Quality Metrics & Reporting Define and track KPIs. Test coverage (code, data, scenarios) Defect leakage rate Automation ROI (time saved vs. maintenance effort) Model accuracy thresholds Report risks and quality trends to stakeholders in sprint reviews. Drive adoption of AI-specific testing tools (e.g., LangChain for LLM testing, Great Expectations for data validation). Soft Skills Strong problem-solving skills for balancing speed and quality in fast-paced AI development. Ability to communicate technical risks to non-technical stakeholders. Collaborative mindset to work with cross-functional teams (data scientists, ML engineers, DevOps). Requirements Technical Requirements Must-Have 5–8 years in test automation, with 2+ years validating AI/ML systems. Expertise in: Automation tools: Selenium, Playwright, Cypress, REST Assured, Locust/JMeter CI/CD: Jenkins, GitHub Actions, GitLab AI/ML testing: Model validation, drift detection, GenAI output evaluation Languages: Python, Java, or JavaScript Certifications: ISTQB Advanced, CAST, or equivalent. Experience with MLOps tools: MLflow, Kubeflow, TFX Familiarity with vector databases (Pinecone, Milvus) and RAG workflows. Strong programming/scripting experience in JavaScript, Python, Java, or similar Experience with API testing, UI testing, and automated pipelines Understanding of AI/ML model testing, output evaluation, and non-deterministic behavior validation Experience with testing AI chatbots, LLM responses, prompt engineering outcomes, or AI fairness/bias Familiarity with MLOps pipelines and automated validation of model performance in production Exposure to Agile/Scrum methodology and tools like Azure Boards Show more Show less

Role name : Scrum Master ( AI & Data Science), AI/ML Scrum Master , Technical Scrum Master – Data & AI Years of exp : 5 - 8 yrs About Dailoqa Dailoqa’s mission is to bridge human expertise and artificial intelligence to solve the challenges facing financial services. Our founding team of 20+ international leaders, including former CIOs and senior industry experts, combines extensive technical expertise with decades of real-world experience to create tailored solutions that harness the power of combined intelligence. With a focus on Financial Services clients, we have deep expertise across Risk & Regulations, Retail & Institutional Banking, Capital Markets, and Wealth & Asset Management. Dailoqa has global reach in UK, Europe, Africa, India, ASEAN, and Australia. We integrate AI into business strategies to deliver tangible outcomes and set new standards for the financial services industry. Working at Dailoqa will be hard work, our environment is fluid and fast-moving and you'll be part of a community that values innovation, collaboration, and relentless curiosity. We’re looking at people who : Are proactive, curious adaptable, and patient Shape the company's vision and will have a direct impact on its success. Have the opportunity for fast career growth. Have the opportunity to participate in the upside of an ultra-growth venture. Have fun 🙂 Don’t apply if: You want to work on a single layer of the application. You prefer to work on well-defined problems. You need clear, pre-defined processes. You prefer a relaxed and slow paced environment. Our Philosophy Small team : Small talented teams outperform large and slow-moving companies. We avoid bureaucracy, keep meetings to a minimum and focus on creating value. Simple where possible: We are passionate about new technology (in particular Machine Learning and AI), but we are more passionate about solving problems for our customers. We strive to find the best solution, be it cutting-edge or old-school. Customer obsessed: We take every opportunity to talk to our customers. We obsess over their problems and work every day to make them happy. Role Overview As a Scrum Master at Dailoqa, you’ll bridge the gap between agile practices and the unique demands of AI/ML-driven product development. You’ll coach cross-functional teams of software engineers, data scientists, and ML engineers to deliver high-impact solutions while fostering a culture of collaboration, experimentation, and continuous improvement. Key Responsibilities Agile Facilitation & Coaching Facilitate all Scrum ceremonies (sprint planning, daily stand-ups, reviews, retrospectives) with a focus on outcomes, not just outputs. Coach team members (software engineers, AI/ML engineers, data scientists) on Agile principles, ensuring adherence to Scrum frameworks while adapting practices to AI/ML workflows. Sprint & Workflow Management Manage hybrid sprints that include software development, data science research, and ML model training/deployment. Maintain Agile boards (Jira, Azure DevOps) to reflect real-time progress, ensuring transparency for stakeholders. Monitor sprint velocity, burndown charts, and cycle times, using metrics to identify bottlenecks and improve predictability. AI/ML-Specific Agile Leadership Adapt Agile practices to AI/ML challenges: Experimentation: Advocate for “spikes” to validate hypotheses or data pipelines. Uncertainty: Help teams embrace iterative learning and fail-fast approaches. Cross-functional collaboration: Resolve dependencies between data engineers, MLops, and product teams. Continuous Improvement Lead retrospectives focused on both technical (e.g., model accuracy, pipeline efficiency) and process improvements. Drive adoption of Agile engineering practices (CI/CD, test automation) tailored to AI/ML workflows. Qualifications ( Must-Have ) 5–8 years as a Scrum Master, with 2+ years supporting AI/ML or data science teams . Deep understanding of Agile frameworks (Scrum, Kanban) and tools (Jira, Azure DevOps). Proven ability to coach teams through AI/ML-specific challenges: Model lifecycle management (training, validation, deployment). Balancing research-heavy work with delivery timelines. Managing data dependencies and computational resource constraints. Certifications: CSM, PSM, or equivalent. Strong understanding of Agile frameworks (Scrum, Kanban) and SDLC principles Experience working with cross-functional teams including data scientists and AI engineers Exposure to AI/ML product development cycles, including research-to-production workflows Familiarity with AI project elements such as model training, data labeling, GenAI experimentation, or MLOps Show more Show less

Role name: Automation Test Lead Years of exp : 5 - 8 yrs About Dailoqa Dailoqa’s mission is to bridge human expertise and artificial intelligence to solve the challenges facing financial services. Our founding team of 20+ international leaders, including former CIOs and senior industry experts, combines extensive technical expertise with decades of real-world experience to create tailored solutions that harness the power of combined intelligence. With a focus on Financial Services clients, we have deep expertise across Risk & Regulations, Retail & Institutional Banking, Capital Markets, and Wealth & Asset Management. Dailoqa has global reach in UK, Europe, Africa, India, ASEAN, and Australia. We integrate AI into business strategies to deliver tangible outcomes and set new standards for the financial services industry. Working at Dailoqa will be hard work, our environment is fluid and fast-moving and you'll be part of a community that values innovation, collaboration, and relentless curiosity. We’re looking at people who: Are proactive, curious adaptable, and patient Shape the company's vision and will have a direct impact on its success. Have the opportunity for fast career growth. Have the opportunity to participate in the upside of an ultra-growth venture. Have fun 🙂 Don’t apply if: You want to work on a single layer of the application. You prefer to work on well-defined problems. You need clear, pre-defined processes. You prefer a relaxed and slow paced environment. Role Overview As an Automation Test Lead at Dailoqa, you’ll architect and implement robust testing frameworks for both software and AI/ML systems. You’ll bridge the gap between traditional QA and AI-specific validation, ensuring seamless integration of automated testing into CI/CD pipelines while addressing unique challenges like model accuracy, GenAI output validation, and ethical AI compliance. Key Responsibilities Test Automation Strategy & Framework Design Design and implement scalable test automation frameworks for frontend (UI/UX), backend APIs, and AI/ML model-serving endpoints using tools like Selenium, Playwright, Postman, or custom Python/Java solutions. Build GenAI-specific test suites for validating prompt outputs, LLM-based chat interfaces, RAG systems, and vector search accuracy. Develop performance testing strategies for AI pipelines (e.g., model inference latency, resource utilization). Continuous Testing & CI/CD Integration Establish and maintain continuous testing pipelines integrated with GitHub Actions, Jenkins, or GitLab CI/CD. Implement shift-left testing by embedding automated checks into development workflows (e.g., unit tests, contract testing). AI/ML Model Validation Collaborate with data scientists to test AI/ML models for accuracy, fairness, stability, and bias mitigation using tools like TensorFlow Model Analysis or MLflow. Validate model drift and retraining pipelines to ensure consistent performance in production. Quality Metrics & Reporting Define and track KPIs: Test coverage (code, data, scenarios) Defect leakage rate Automation ROI (time saved vs. maintenance effort) Model accuracy thresholds Report risks and quality trends to stakeholders in sprint reviews. Drive adoption of AI-specific testing tools (e.g., LangChain for LLM testing, Great Expectations for data validation). Technical Requirements Must-Have 5–8 years in test automation, with 2+ years validating AI/ML systems. Expertise in: Automation tools: Selenium, Playwright, Cypress, REST Assured, Locust/JMeter CI/CD: Jenkins, GitHub Actions, GitLab AI/ML testing: Model validation, drift detection, GenAI output evaluation Languages: Python, Java, or JavaScript Certifications: ISTQB Advanced, CAST, or equivalent. Experience with MLOps tools: MLflow, Kubeflow, TFX Familiarity with vector databases (Pinecone, Milvus) and RAG workflows. Strong programming/scripting experience in JavaScript, Python, Java, or similar Experience with API testing, UI testing, and automated pipelines Understanding of AI/ML model testing, output evaluation, and non-deterministic behavior validation Experience with testing AI chatbots, LLM responses, prompt engineering outcomes, or AI fairness/bias Familiarity with MLOps pipelines and automated validation of model performance in production Exposure to Agile/Scrum methodology and tools like Azure Boards Soft Skills Strong problem-solving skills for balancing speed and quality in fast-paced AI development. Ability to communicate technical risks to non-technical stakeholders. Collaborative mindset to work with cross-functional teams (data scientists, ML engineers, DevOps). Show more Show less

Years of exp : 10 - 15 yrs Location : Noida Join us as Cloud Engineer at Dailoqa , where you will be responsible for operationalizing cutting-edge machine learning and generative AI solutions, ensuring scalable, secure, and efficient deployment across infrastructure. You will work closely with data scientists, ML engineers, and business stakeholders to build and maintain robust MLOps pipelines, enabling rapid experimentation and reliable production implementation of AI models, including LLMs and real-time analytics systems. To be successful as Cloud Engineer you should have experience with: Cloud sourcing, networks, VMs, performance, scaling, availability, storage, security, access management Deep expertise in one or more cloud platforms: AWS, Azure, GCP Strong experience in containerization and orchestration (Docker, Kubernetes, Helm) Familiarity with CI/CD tools: GitHub Actions, Jenkins, Azure DevOps, ArgoCD, etc. Proficiency in scripting languages (Python, Bash, PowerShell) Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML Strong understanding of DevOps principles applied to ML workflows. Key Responsibilities may include: Design and implement scalable, cost-optimized, and secure infrastructure for AI-driven platforms. Implement infrastructure as code using tools like Terraform, ARM, or Cloud Formation. Automate infrastructure provisioning, CI/CD pipelines, and model deployment workflows. Ensure version control, repeatability, and compliance across all infrastructure components. Set up monitoring, logging, and alerting frameworks using tools like Prometheus, Grafana, ELK, or Azure Monitor. Optimize performance and resource utilization of AI workloads including GPU-based training/inference Experience with Snowflake, Databricks for collaborative ML development and scalable data processing. Understanding model interpretability, responsible AI, and governance. Contributions to open-source MLOps tools or communities. Strong leadership, communication, and cross-functional collaboration skills. Knowledge of data privacy, model governance, and regulatory compliance in AI systems. Exposure to LangChain, Vector DBs (e. g. , FAISS, Pinecone), and retrieval-augmented generation (RAG) pipelines. Show more Show less

About Us  Dailoqa is an international, AI-native company focused on solving business problems for clients in the Financial Services sector by combining assets and strong technical and functional skills. We help our clients in their future as an AI-native organisation by providing roadmap for unlocking value by Combined Intelligence in partnership between humans and agents in integrating new technology into complex and legacy IT architectures. Key Responsibilities: Data Analysis and Modelling: Collect, process, and analyse large datasets to extract actionable insights. Develop and implement statistical and machine learning models to solve complex business problems. Algorithm Development: Design and develop algorithms for data mining, predictive modelling, and other data-driven applications. Continuously improve and optimize algorithms for better performance. Visualization and Reporting: Create data visualizations and reports to effectively communicate insights and findings to stakeholders. Develop dashboards and interactive tools for data exploration. Collaboration: Work closely with cross-functional teams, including data engineers, AI engineers, and product managers, to understand project requirements and deliver high-quality solutions. Data Preparation: Perform data cleaning, transformation, and augmentation to ensure data quality and readiness for analysis. Implement ETL processes to streamline data workflows. Machine Learning Implementation: Develop and deploy machine learning models to production environments. Monitor model performance and implement necessary updates and improvements. Documentation and Reporting: Document the data analysis process, including data sources, methodologies, and results. Prepare and present reports on project progress and findings to stakeholders. Ethical Data Practices: Ensure that data analysis and modelling adhere to ethical standards and guidelines, promoting fairness and minimizing bias. Continuous Learning and Improvement: Stay updated with the latest advancements in data science and machine learning. Attend conferences, read research papers, and participate in professional development activities to continuously enhance skills and knowledge. Problem-Solving and Innovation: Identify and solve complex problems using innovative data-driven solutions. Propose and implement creative ideas to leverage data for various applications and industries. Testing and Validation: Conduct rigorous testing and validation of models and algorithms to ensure accuracy, reliability, and scalability. Implement A/B testing and other validation techniques to assess the real-world performance of data-driven solutions. Feedback Incorporation: Collect and analyse feedback from users and stakeholders to improve data models and applications. Iterate on model development based on user needs and project requirements. Skills and Qualifications: Technical Skills: Proficiency in programming languages such as Python, R, and SQL. Strong knowledge of machine learning frameworks and libraries, including TensorFlow, PyTorch, and Scikit-learn. Experience with data visualization tools such as Tableau, Power BI, and Matplotlib. Familiarity with big data technologies, including Hadoop and Spark. Knowledge of data pre-processing, feature engineering, and ETL processes. Mathematics and Statistics: Strong foundation in linear algebra, calculus, and probability & statistics. Soft Skills: Excellent problem-solving and critical thinking skills. Strong communication skills for explaining complex technical concepts to non-technical stakeholders. Ability to work effectively in a team and collaborate with cross-functional teams. Commitment to continuous learning and staying updated with industry advancements. Creativity and innovation in developing data-driven solutions. Domain Knowledge: Understanding of the Financial Services industry and its specific challenges. Awareness of ethical considerations in data science and machine learning. Overall and Relevant Experience: 3+ years overall IT experience with at least 2+ years of relevant experience in data science with Financial Services organisations. Bachelor’s degree in computer science, Engineering, Statistics, Mathematics, or a related quantitative field. Why Join Us: Innovative Environment: Work on cutting-edge AI technologies and innovative projects. International Exposure : To work with international clients/team Collaborative Culture: Be part of a passionate and supportive team. Professional Growth: Opportunities for continuous learning and development. Impactful Work: Contribute to meaningful projects that drive innovation and make a difference. If you are excited about the prospect of working in a dynamic and forward-thinking company, we would love to hear from you!

Years of exp :10 - 15 yrs Location : Noida Join us as Cloud Engineer Lead at Dailoqa , where you will be responsible for operationalizing cutting-edge machine learning and generative AI solutions, ensuring scalable, secure, and efficient deployment across infrastructure. You will work closely with data scientists, ML engineers, and business stakeholders to build and maintain robust MLOps pipelines, enabling rapid experimentation and reliable production implementation of AI models, including LLMs and real-time analytics systems. To be successful as Cloud Engineer you should have experience with: Cloud sourcing, networks, VMs, performance, scaling, availability, storage, security, access management Deep expertise in one or more cloud platforms: AWS, Azure, GCP Strong experience in containerization and orchestration (Docker, Kubernetes, Helm) Familiarity with CI/CD tools: GitHub Actions, Jenkins, Azure DevOps, ArgoCD, etc. Proficiency in scripting languages (Python, Bash, PowerShell) Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML Strong understanding of DevOps principles applied to ML workflows. Key Responsibilities may include: Design and implement scalable, cost-optimized, and secure infrastructure for AI-driven platforms. Implement infrastructure as code using tools like Terraform, ARM, or Cloud Formation. Automate infrastructure provisioning, CI/CD pipelines, and model deployment workflows. Ensure version control, repeatability, and compliance across all infrastructure components. Set up monitoring, logging, and alerting frameworks using tools like Prometheus, Grafana, ELK, or Azure Monitor. Optimize performance and resource utilization of AI workloads including GPU-based training/inference Experience with Snowflake, Databricks for collaborative ML development and scalable data processing. Understanding model interpretability, responsible AI, and governance. Contributions to open-source MLOps tools or communities. Strong leadership, communication, and cross-functional collaboration skills. Knowledge of data privacy, model governance, and regulatory compliance in AI systems. Exposure to LangChain, Vector DBs (e. g. , FAISS, Pinecone), and retrieval-augmented generation (RAG) pipelines.

Role name : Scrum Master ( AI & Data Science), AI/ML Scrum Master , Technical Scrum Master – Data & AI Years of exp : 9 - 12 yrs Location : Noida Role Overview As a Scrum Master at Dailoqa, you’ll bridge the gap between agile practices and the unique demands of AI/ML-driven product development. You’ll coach cross-functional teams of software engineers, data scientists, and ML engineers to deliver high-impact solutions while fostering a culture of collaboration, experimentation, and continuous improvement. Key Responsibilities Agile Facilitation & Coaching Facilitate all Scrum ceremonies (sprint planning, daily stand-ups, reviews, retrospectives) with a focus on outcomes, not just outputs. Coach team members (software engineers, AI/ML engineers, data scientists) on Agile principles, ensuring adherence to Scrum frameworks while adapting practices to AI/ML workflows. Sprint & Workflow Management Manage hybrid sprints that include software development, data science research, and ML model training/deployment. Maintain Agile boards (Jira, Azure DevOps) to reflect real-time progress, ensuring transparency for stakeholders. Monitor sprint velocity, burndown charts, and cycle times, using metrics to identify bottlenecks and improve predictability. AI/ML-Specific Agile Leadership Adapt Agile practices to AI/ML challenges: Experimentation: Advocate for “spikes” to validate hypotheses or data pipelines. Uncertainty: Help teams embrace iterative learning and fail-fast approaches. Cross-functional collaboration: Resolve dependencies between data engineers, MLops, and product teams. Continuous Improvement Lead retrospectives focused on both technical (e.g., model accuracy, pipeline efficiency) and process improvements. Drive adoption of Agile engineering practices (CI/CD, test automation) tailored to AI/ML workflows. Qualifications Must-Have 5–8 years as a Scrum Master, with 2+ years supporting AI/ML or data science teams . Deep understanding of Agile frameworks (Scrum, Kanban) and tools (Jira, Azure DevOps). Proven ability to coach teams through AI/ML-specific challenges: Model lifecycle management (training, validation, deployment). Balancing research-heavy work with delivery timelines. Managing data dependencies and computational resource constraints. Certifications: CSM, PSM, or equivalent. • Strong understanding of Agile frameworks (Scrum, Kanban) and SDLC principles • Experience working with cross-functional teams including data scientists and AI engineers • Exposure to AI/ML product development cycles, including research-to-production workflows • Familiarity with AI project elements such as model training, data labeling, GenAI experimentation, or MLOps

Role Overview As a Test Automation Lead at Dailoqa, you’ll architect and implement robust testing frameworks for both software and AI/ML systems. You’ll bridge the gap between traditional QA and AI-specific validation, ensuring seamless integration of automated testing into CI/CD pipelines while addressing unique challenges like model accuracy, GenAI output validation, and ethical AI compliance. Key Responsibilities Test Automation Strategy & Framework Design Design and implement scalable test automation frameworks for frontend (UI/UX) , backend APIs , and AI/ML model-serving endpoints using tools like Selenium, Playwright, Postman, or custom Python/Java solutions. Build GenAI-specific test suites for validating prompt outputs, LLM-based chat interfaces, RAG systems, and vector search accuracy. Develop performance testing strategies for AI pipelines (e.g., model inference latency, resource utilization). Continuous Testing & CI/CD Integration Establish and maintain continuous testing pipelines integrated with GitHub Actions, Jenkins, or GitLab CI/CD. Implement shift-left testing by embedding automated checks into development workflows (e.g., unit tests, contract testing). AI/ML Model Validation Collaborate with data scientists to test AI/ML models for accuracy , fairness , stability , and bias mitigation using tools like TensorFlow Model Analysis or MLflow. Validate model drift and retraining pipelines to ensure consistent performance in production. Quality Metrics & Reporting Define and track KPIs: Test coverage (code, data, scenarios) Defect leakage rate Automation ROI (time saved vs. maintenance effort) Model accuracy thresholds Report risks and quality trends to stakeholders in sprint reviews. Drive adoption of AI-specific testing tools (e.g., LangChain for LLM testing, Great Expectations for data validation). Technical Requirements Must-Have 7–10 years in test automation, with 2+ years validating AI/ML systems . Expertise in: Automation tools : Selenium, Playwright, Cypress, REST Assured, Locust/JMeter CI/CD : Jenkins, GitHub Actions, GitLab AI/ML testing : Model validation, drift detection, GenAI output evaluation Languages : Python, Java, or JavaScript Certifications: ISTQB Advanced, CAST, or equivalent. Experience with MLOps tools : MLflow, Kubeflow, TFX Familiarity with vector databases (Pinecone, Milvus) and RAG workflows . Strong programming/scripting experience in JavaScript, Python, Java, or similar Experience with API testing, UI testing, and automated pipelines • Understanding of AI/ML model testing, output evaluation, and non-deterministic behavior validation • Experience with testing AI chatbots, LLM responses, prompt engineering outcomes, or AI fairness/bias • Familiarity with MLOps pipelines and automated validation of model performance in production • Exposure to Agile/Scrum methodology and tools like Azure Boards Soft Skills Strong problem-solving skills for balancing speed and quality in fast-paced AI development. Ability to communicate technical risks to non-technical stakeholders. Collaborative mindset to work with cross-functional teams (data scientists, ML engineers, DevOps).

Role name: Automation Test Lead (AI/ML) Years of exp: 5 - 8 yrs About Dailoqa Dailoqa’s mission is to bridge human expertise and artificial intelligence to solve the challenges facing financial services. Our founding team of 20+ international leaders, including former CIOs and senior industry experts, combines extensive technical expertise with decades of real-world experience to create tailored solutions that harness the power of combined intelligence. With a focus on Financial Services clients, we have deep expertise across Risk & Regulations, Retail & Institutional Banking, Capital Markets, and Wealth & Asset Management. Dailoqa has global reach in UK, Europe, Africa, India, ASEAN, and Australia. We integrate AI into business strategies to deliver tangible outcomes and set new standards for the financial services industry. Working at Dailoqa will be hard work, our environment is fluid and fast-moving and you'll be part of a community that values innovation, collaboration, and relentless curiosity. We’re looking at people who: Are proactive, curious adaptable, and patient Shape the company's vision and will have a direct impact on its success. Have the opportunity for fast career growth. Have the opportunity to participate in the upside of an ultra-growth venture. Have fun 🙂 Don’t apply if: You want to work on a single layer of the application. You prefer to work on well-defined problems. You need clear, pre-defined processes. You prefer a relaxed and slow paced environment. Role Overview As an Automation Test Lead at Dailoqa, you’ll architect and implement robust testing frameworks for both software and AI/ML systems. You’ll bridge the gap between traditional QA and AI-specific validation, ensuring seamless integration of automated testing into CI/CD pipelines while addressing unique challenges like model accuracy, GenAI output validation, and ethical AI compliance. Key Responsibilities Test Automation Strategy & Framework Design Design and implement scalable test automation frameworks for frontend (UI/UX), backend APIs, and AI/ML model-serving endpoints using tools like Selenium, Playwright, Postman, or custom Python/Java solutions. Build GenAI-specific test suites for validating prompt outputs, LLM-based chat interfaces, RAG systems, and vector search accuracy. Develop performance testing strategies for AI pipelines (e.g., model inference latency, resource utilization). Continuous Testing & CI/CD Integration Establish and maintain continuous testing pipelines integrated with GitHub Actions, Jenkins, or GitLab CI/CD. Implement shift-left testing by embedding automated checks into development workflows (e.g., unit tests, contract testing). AI/ML Model Validation Collaborate with data scientists to test AI/ML models for accuracy, fairness, stability, and bias mitigation using tools like TensorFlow Model Analysis or MLflow. Validate model drift and retraining pipelines to ensure consistent performance in production. Quality Metrics & Reporting Define and track KPIs: Test coverage (code, data, scenarios) Defect leakage rate Automation ROI (time saved vs. maintenance effort) Model accuracy thresholds Report risks and quality trends to stakeholders in sprint reviews. Drive adoption of AI-specific testing tools (e.g., LangChain for LLM testing, Great Expectations for data validation). Technical Requirements Must-Have 5–8 years in test automation, with 2+ years validating AI/ML systems. Expertise in: Automation tools: Selenium, Playwright, Cypress, REST Assured, Locust/JMeter CI/CD: Jenkins, GitHub Actions, GitLab AI/ML testing: Model validation, drift detection, GenAI output evaluation Languages: Python, Java, or JavaScript Certifications: ISTQB Advanced, CAST, or equivalent. Experience with MLOps tools: MLflow, Kubeflow, TFX Familiarity with vector databases (Pinecone, Milvus) and RAG workflows. Strong programming/scripting experience in JavaScript, Python, Java, or similar Experience with API testing, UI testing, and automated pipelines Understanding of AI/ML model testing, output evaluation, and non-deterministic behavior validation Experience with testing AI chatbots, LLM responses, prompt engineering outcomes, or AI fairness/bias Familiarity with MLOps pipelines and automated validation of model performance in production Exposure to Agile/Scrum methodology and tools like Azure Boards Soft Skills Strong problem-solving skills for balancing speed and quality in fast-paced AI development. Ability to communicate technical risks to non-technical stakeholders. Collaborative mindset to work with cross-functional teams (data scientists, ML engineers, DevOps).

Job Title: Functional Tester – Agentic AI Solution (Banking & Wealth Management) Location: Noida Experience Level: 2–6 years Reports To: QA Lead / Product Manager We are looking for an experienced Functional Tester to validate the business workflows, compliance logic, and customer-facing features of an Agentic AI solution built for the banking and wealth management domain. This solution leverages LLMs, retrieval-augmented generation (RAG), and domain ontologies to provide intelligent data retrieval, client interaction, and regulatory compliance automation. Key Responsibilities Functional Testing • Understand functional and non-functional requirements for Agentic AI features (e.g., document Q&A, investment summarization, client onboarding, compliance tracking) • Validate end-to-end user journeys across banking and wealth advisory use cases • Test conversational workflows and agentic logic flows (multi-step LLM tasks) • Verify outputs from LLMs for relevance, consistency, and context-awareness • Ensure correct role-based access across advisors, relationship managers, and compliance officers Test Planning & Execution • Prepare detailed test plans, test cases, and traceability matrices from EPICs, features, and user stories • Execute manual testing for user interfaces, APIs, and chat/agent interactions • Perform regression testing across AI pipelines after prompt or model updates • Log, track, and retest defects using tools like Azure DevOps, Jira, or TestRail AI Model & RAG Testing • Validate grounding of LLM responses from vector databases (e.g., Azure AI Search, Pinecone) • Check performance of RAG pipelines including hallucination mitigation, context window boundaries, and fallback logic • Test interaction of agent orchestration flows (LangChain/Autogen/semantic kernel) • Verify prompt-based test cases for accuracy and coverage Integration & Data Testing • Validate PDF/statement extraction logic from custodians (e.g., Morgan Stanley, Pershing) • Check structured data output (JSON, tables) from Azure Form Recognizer or OpenAI pipelines • Ensure data integrity and lineage from ingestion → processing → user response Must-Have Skills & Tools Category Tools/Tech Test Management Azure / AWA DevOps, Jira, TestRail, Azure Board Test Types Functional, UI, Integration, AI behavior Domain Knowledge Wealth Mgmt workflows, KYC, portfolio views, regulatory rules AI Concepts LLM output validation, RAG testing, prompt engineering basics Data JSON validation, SQL for test data setup Tools (Optional) Postman, Python (for test automation scripts), Excel-based test mapping Preferred Qualifications • Bachelor’s degree in Engineering, Finance, or Computer Science • Experience testing AI/ML solutions or conversational AI products • Familiarity with Azure AI stack, OpenAI, or AWS Bedrock • Exposure to compliance-heavy domains like banking, wealth, or insurance • Ability to work with cross-functional Agile teams (product, engineering, AI/ML, compliance) Nice-to-Have Skills • Experience writing prompt-based test cases for LLMs • Familiarity with agentic workflows (e.g., task chaining, autonomy testing) • Understanding of financial statement formats (portfolio summaries, trade confirms) • Basic scripting to validate data output (e.g., Python, bash)

About the Role We are looking for Sr.AI and Machine Learning engineer who want to help shape the future of Financial Services clients and our company. As part of the team, you will get to · Work directly with our founding team and be a core member. · Apply the latest AI techniques to solve real problems faced by Financial Services clients. · Design, build, and refine datasets to evaluate and continuously improve our solutions. · Participate in strategy and product ideation sessions, influencing our product and solution roadmap. Key Responsibilities · Agentic AI Development : Work on building scalable multi-modal Large Language Model (LLM) based AI agents, leveraging frameworks such as LangGraph, Microsoft Autogen, or Crewai. · AI Research and Innovation : Research and build innovative solutions to relevant AI problems, including Retrieval-Augmented Generation (RAG), semantic search, knowledge representation, tool usage, fine-tuning, and reasoning in LLMs. · Technical Expertise : Proficiency in a technology stack that includes Python, LlamaIndex / LangChain, PyTorch, HuggingFace, FastAPI, Postgres, SQLAlchemy, Alembic, OpenAI, Docker, Azure, Typescript, and React. · LLM and NLP Experience : Hands-on experience working with LLMs, RAG architectures, Natural Language Processing (NLP), or applying Machine Learning to solve real-world problems. · Dataset Development : Strong track record of building datasets for training and/or evaluating machine learning models. · Customer Focus : Enjoy diving deep into the domain, understanding the problem, and focusing on delivering value to the customer. · Adaptability : Thrive in a fast-paced environment and are excited about joining an early-stage venture. · Model Deployment and Management : Automate model deployment, monitoring, and retraining processes. · Collaboration and Optimization : Collaborate with data scientists to review, refactor, and optimize machine learning code. · Version Control and Governance : Implement version control and governance for models and data. Required Qualifications: · Bachelor's degree in computer science, Software Engineering, or a related field · 4-8 years of experience in MLOps, DevOps, or related roles Have strong programming experience and familiarity with Python based deep learning frameworks like Pytorch, JAX, Tensorflow Have strong familiarity and knowledge of machine learning concepts · Proficiency in cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code tools like Terraform Desired Skills: · Experience with experiment tracking and model versioning tools You have experience with technology stack: Python, LlamaIndex / LangChain, PyTorch, HuggingFace, FastAPI, Postgres, SQLAlchemy, Alembic, OpenAI, Docker, Azure, Typescript, React. · Knowledge of data pipeline orchestration tools like Apache Airflow or Prefect · Familiarity with software testing and test automation practices · Understanding of ethical considerations in machine learning deployments · Strong problem-solving skills and ability to work in a fast-paced environment

Key Responsibilities Lead, mentor, and guide a team of Python developers to deliver scalable and maintainable solutions. Design end-to-end Python applications with a strong focus on performance, scalability, and security. Define technical strategies, coding standards, and development best practices for the team. Collaborate with cross-functional teams including Product, Design, and QA to deliver features as per roadmap. Oversee code reviews, testing frameworks, CI/CD pipelines, and ensure quality standards are maintained. Work closely with stakeholders to understand requirements, estimate effort, and manage project execution using Agile methodologies. Provide technical solutions for complex problems and troubleshoot issues effectively. Stay current with Python advancements, frameworks, and relevant technologies to drive innovation. Manage task distribution, timelines, and team productivity across projects. Required Technical Skills Proficient in Python and related libraries Deep knowledge of Python frameworks such as Django, Flask, or FastAPI. Expertise in developing RESTful APIs and integrating with third-party services. Experience with cloud platforms like AWS, GCP, or Azure. Familiarity with containerization tools like Docker and Kubernetes. Strong background in database and cache systems - SQL and NoSQL. Working knowledge of CI/CD, unit testing frameworks, and code versioning tools (Git). Understanding of Agile/Scrum methodologies and DevOps practices. Team & Project Management Experience in mentoring and managing a team of developers. Ability to manage sprints, timelines, task assignments, and client communication. Excellent problem-solving abilities and a structured approach to debugging and root cause analysis. Strong documentation habits for APIs, deployment processes, and codebase architecture. Qualifications Bachelor's or Master's degree in Computer Science, Engineering, or related field. 7+ years of hands-on experience in Python development, with at least 2+ years in a technical lead role. Strong grasp of design patterns and clean coding practices. Effective communicator with the ability to present solutions to both technical and non-technical audiences.

POSITION NAME : AI and Machine Learning Engineer Location: Noida, NCR Years of Experience: 4-8 Yrs About Dailoqa Dailoqa’s mission is to bridge human expertise and artificial intelligence to solve the challenges facing financial services. Our founding team of 20+ international leaders, including former CIOs and senior industry experts, combines extensive technical expertise with decades of real-world experience to create tailored solutions that harness the power of combined intelligence. With a focus on Financial Services clients we have deep expertise across Risk & Regulations, Retail & Institutional Banking, Capital Markets, and Wealth & Asset Management. Dailoqa has global reach in UK, Europe, Africa, India, ASEAN, and Australia. We integrate AI into business strategies to deliver tangible outcomes and set new standards for the financial services industry. Working at Dailoqa will be hard work, our environment is fluid and fast-moving and you'll be part of a community that values innovation, collaboration, and relentless curiosity. We’re looking at people who : · Are proactive, curious adaptable, and patient · Shape the company's vision and will have a direct impact on its success. · Have the opportunity for fast career growth. · Have the opportunity to participate in the upside of an ultra-growth venture. · Have fun 🙂 Don’t apply if: · You want to work on a single layer of the application. · You prefer to work on well-defined problems. · You need clear, pre-defined processes. · You prefer a relaxed and slow paced environment. Our Philosophy Small team : Small talented teams outperform large and slow-moving companies. We avoid bureaucracy, keep meetings to a minimum and focus on creating value. Simple where possible: We are passionate about new technology (in particular Machine Learning and AI), but we are more passionate about solving problems for our customers. We strive to find the best solution, be it cutting-edge or old-school. Customer obsessed: We take every opportunity to talk to our customers. We obsess over their problems and work every day to make them happy. About the Role We are looking for AI and Machine Learning engineer who want to help shape the future of Financial Services clients and our company. As part of the team, you will get to · Work directly with our founding team and be a core member. · Apply the latest AI techniques to solve real problems faced by Financial Services clients. · Design, build, and refine datasets to evaluate and continuously improve our solutions. · Participate in strategy and product ideation sessions, influencing our product and solution roadmap. Key Responsibilities · Agentic AI Development : Work on building scalable multi-modal Large Language Model (LLM) based AI agents, leveraging frameworks such as LangGraph, Microsoft Autogen, or Crewai. · AI Research and Innovation : Research and build innovative solutions to relevant AI problems, including Retrieval-Augmented Generation (RAG), semantic search, knowledge representation, tool usage, fine-tuning, and reasoning in LLMs. · Technical Expertise : Proficiency in a technology stack that includes Python, LlamaIndex / LangChain, PyTorch, HuggingFace, FastAPI, Postgres, SQLAlchemy, Alembic, OpenAI, Docker, Azure, Typescript, and React. · LLM and NLP Experience : Hands-on experience working with LLMs, RAG architectures, Natural Language Processing (NLP), or applying Machine Learning to solve real-world problems. · Dataset Development : Strong track record of building datasets for training and/or evaluating machine learning models. · Customer Focus : Enjoy diving deep into the domain, understanding the problem, and focusing on delivering value to the customer. · Adaptability : Thrive in a fast-paced environment and are excited about joining an early-stage venture. · Model Deployment and Management : Automate model deployment, monitoring, and retraining processes. · Collaboration and Optimization : Collaborate with data scientists to review, refactor, and optimize machine learning code. · Version Control and Governance : Implement version control and governance for models and data. Required Qualifications: · Bachelor's degree in computer science, Software Engineering, or a related field · 4-8 years of experience in MLOps, DevOps, or related roles Have strong programming experience and familiarity with Python based deep learning frameworks like Pytorch, JAX, Tensorflow Have strong familiarity and knowledge of machine learning concepts · Proficiency in cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code tools like Terraform Desired Skills: · Experience with experiment tracking and model versioning tools You have experience with technology stack: Python, LlamaIndex / LangChain, PyTorch, HuggingFace, FastAPI, Postgres, SQLAlchemy, Alembic, OpenAI, Docker, Azure, Typescript, React. · Knowledge of data pipeline orchestration tools like Apache Airflow or Prefect · Familiarity with software testing and test automation practices · Understanding of ethical considerations in machine learning deployments · Strong problem-solving skills and ability to work in a fast-paced environment