GEN AI (NCS/Job/ 2452)

Quality Engineering GenAI Consultant – GenAi Automation Testing

Job Profile

 

Blend of domain expertise in Testing - Quality Engineering with experience and hands on with Generative AI technologies as well coding with the key objective of developing, implementing GenAI use cases across STLC – testing lifecycle.

In this role, the Consultant will be responsible for mapping the STLC, activities – dependencies within STLC, identifying opportunities to bring GenAi in the loop, breaking down the problems into granular chunks, formulating outputs and reverse engineering inputs and analysis mechanisms with GenAI to augment, enhance, automate and orchestrate relevant activities for day in a life of tester and/or in Software Testing lifecycle. Prioritize the use cases for piloting groom the use cases in the client context, with technology and ways of working within the landscape and exploring use of Generative AI technologies that client has subscribed for. Formulate designs for integrations with relevant Testing tools, DevOps pipelines for exchanging inputs and outputs from GenAI engines.

Expertise in LLMs and specific libraries within LLMs with inputs ranging from text, images, media and formulating the business logic, process flow and produce specific outputs that align with the test flow, test tools in variety of formats – templates that is grounded to the specified context.

Experience in multiple LLMs and their libraries, Embeddings, Retrieval-Augmented Generation (RAG) techniques, context-aware grounded generation to create comprehensive approach for addressing use cases in testing and quality engineering.

QE GenAi consultant will be solely responsible and accountable for identifying – enabling and operationalizing – implementing use cases with GenAi for testers with well defined inputs – outcomes and business logic for testers delivering across centralized, federated and agile ways of working.

Key skills:

1. Total experience in testing

a. C1: 12+ years

2. Break up of experience:

a. Overall Testing – Quality engineering experience spanning across domains of BFSI / Retail / Health Care / Telecom….: 5+ years as above for ALL bands

b. Hands on role of Test lead, Test automation, Selenium Java/C#/Python (5+ years) across variety of testing programs, and automation frameworks, BDD

c. Data science - Python (2+ years) Major libraries in Python for AI/ ML, StreamLit pandas, transformers, sklearn having solved real life problems for clients - domains

d. Recent technologies of Generative AI (1+ years) (NOT co-pilot) either of Azure OpenAI, AWS Bedrock, Google Gemini, Llama, Mistral…

3. Programming and Scripting: Proficiency in programming languages used for Test automation, Test Automation Framework creation. Ability to write scripts for test automation and data generation and handling test dependencies like mocking.

4. Data Management and Vector Database: Skills in data handling, preprocessing, and manipulation. Proficiency in managing vector databases embeddings, designing and optimizing indexing strategies, efficient retrieval. Hands on with Pinecone and/or Opensearch and/or Chroma

5. Testing and Quality Engineering: Strong background in software testing principles and methodologies. Experience in creating test plans, test cases, and executing manual and automated tests. Familiarity with

various testing types, including functional, regression, performance, and security testing. Having handled complex test programs, integrations with CICD – DevOps pipelines…

6. Test Transformation Consulting: Awareness on understanding the current state and creating roadmap for testing transformation, typical challenges, ways of working, delivery constructs, centralized – federated models of test teams, tools & frameworks for test automation and adjacent areas, shift left, shift right

7. Industry Domain Testing experience: Understanding of two or more industry or domain for which testing is being performed. Like Banking, Retail, Telecom, Health, Insurance,... Awareness of specific quality and compliance standards relevant to the domain.

8. Industry LLMs: experience in working with two or more open source / licensed: ChatGPT, Azure OpenAI, VertexAi, Plam, Llama, Falcon, Hugging face, Sagemaker, … Basic understanding of Model Architecture and how it processes and generates content.

9. Implementation experience in the retail/consumer sector for min 2-3 projects

10. Retrieval augmented generation – RAG: Extensive experience if using RAG models on specific datasets or domains to improve and reference outcomes for testing and quality engineering tasks

11. Contextual Grounding: Ability to ground entities in context, considering the dynamic nature of information and the evolving context.

12. Ethical and Security, Data Privacy Considerations: Awareness of ethical considerations in AI, including bias mitigation and responsible AI practices and data privacy, security elements

Industry skills:

1. Pipeline Integration: Proficiency in integrating into existing testing and quality engineering pipelines.

2. Problem-Solving and Analytical Skills: Ability to analyze complex systems and identify areas for improvement in testing processes. Strong problem-solving skills to address issues related to test automation and generative AI.

3. Communication Skills: Effective communication to collaborate with cross-functional teams, including developers, QA engineers, and business stakeholders. Ability to communicate complex technical concepts to non-technical audiences.

4. Continuous Learning: A mindset of continuous learning to stay updated on the latest advancements in AI, testing methodologies, and industry trends.

5. Collaboration, Teamwork, Documentation, version control: Ability to work collaboratively in a team environment and contribute to a positive and productive work culture. Strong documentation skills for creating and maintaining assets and all documentation.