AI developers who can built agents for Quality and testing.

Role Requirements:

• • Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or related field
  • 3+ years of experience in AI/ML/NLP development, preferably using Python, TensorFlow, PyTorch, or Hugging Face Transformers
  • Hands-on experience in building LLM-based applications, including prompt engineering, fine-tuning, and RAG
  • Familiarity with quality engineering concepts such as test design, automation frameworks (e.g., Selenium, Cypress), and defect lifecycle
  • Experience with vector databases (e.g., FAISS, Weaviate, Pinecone) and embeddings
  • Proficiency in RESTful APIs, Docker, Git, and cloud platforms (AWS, Azure, or Google Cloud Platform)8+ years of experience in Machine learning engineering /MLE , data science and data engineering, with a proven track record of

    Looks like they need AI developers who can built agents for Quality and testing.

    Need hard core AI developers who can develop Agentic systems to do QE work instead of actual testers

     

    About the Role

     

    We are looking for innovative and highly skilled AI Developers to design, develop, and deploy agentic AI solutions that automate and augment Quality Engineering processes.

    You will work closely with QE architects, product managers, and data scientists to build autonomous and assistive agents such as:

    • Knowledge Agents for contextual retrieval and decision support
    • Test Case Generation Agents powered by LLMs and domain ontologies
    • Test Script Generation Agents that convert user stories into executable automation
    • Defect Triage Agents that analyze logs, cluster bugs, and suggest fixes
    • Risk-based Prioritization Agents for intelligent test selection and planning

    Key Responsibilities

    • Design and implement AI-driven agents that support end-to-end quality engineering lifecycle
    • Fine-tune and deploy LLMs and retrieval-augmented generation (RAG) pipelines for specific QE tasks
    • Leverage multi-modal and multi-agent systems for code, test, and defect generation
    • Collaborate with QE teams to understand domain-specific needs and optimize agent performance
    • Integrate agents into enterprise CI/CD pipelines and DevOps toolchains
    • Continuously improve agent capabilities using feedback loops, reinforcement learning, or human-in-the-loop strategies
    • Conduct evaluations and benchmark performance of AI agents using QE KPIs

  • Preferred Qualifications

  • Experience in autonomous agents or agent frameworks (e.g., LangChain, AutoGen, CrewAI)
  • Knowledge of software testing tools (like JIRA, TestRail, Postman, etc.)
  • Understanding of DevOps pipelines and tools (e.g., Jenkins, GitHub Actions)
  • Exposure to MLOps practices for continuous learning and model deployment
  • Background in reinforcement learning or graph-based reasoning
  delivering impactful solutions.
• Proven experience in AI Application & Infrastructure Optimization in terms of Capacity, Performance and Cost
• Architectural Optimization - Experience in optimization of solution architectures with a focus on data security, privacy, application reliability, infrastructure scalability, and cost-efficiency for Data Science and Generative AI platforms.
• Scalable AI Solution Deployment - Experience in designing and implementing scalable AI and Data Science solutions capable of handling large-scale, distributed data and compute workloads across hybrid and cloud-native environments(OCI).
• Performance Tuning for AI/ML and GenAI Workloads - Experience in hyperparameter tuning and performance optimization for AI/ML and Generative AI models to maximize accuracy, efficiency, and resource utilization.
• AI Lifecycle Management - Experience in robust AI pipelines for model versioning, testing, validation, and deployment, ensuring seamless integration into production environments with CI/CD best practices.
• AI Full stack : Experience in in any full stack /development experience in Java, or .Net or React, Node.js, Flask/Django, REST APIs.
• Proficiency in Python, SQL, and machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Experience in EDA, data engineering, creating data pipelines and processing huge volume of data.
• Understanding of cloud platforms (OCI, Azure) and containerization (Docker, Kubernetes).
• Excellent problem-solving, communication, and stakeholder management skills.
• Prior experience in a customer-facing or product-oriented role.
• Master s in computer science, Machine Learning , Data Science, Statistics, or a related field.