Qualcomm India Private Limited
Operations Group, Operations Group > Program Analyst
General Summary:
Assists with program development and implementation through managing processes, procedures, and tools that improve efficiencies. A Program Analyst coordinates across teams and monitors timelines, budgets, risks, and priorities to achieve program progress. Typically, a program needing a Program Analyst will be of significant size and will require expertise related to the development of project management mechanisms.
Minimum Qualifications:
- Associate's degree in Business Administration, Management, Computer Science, Engineering, Computer Science, or related field.
OR
High School Diploma or equivalent and 2+ years of relevant work experience.
Job Title: Program Analyst Sr – Generative AI, Prompt Engineering & Validation
Location: Bangalore
Experience: 2–6 years
Employment Type: Full-time
About the Role
We are looking for a proactive and detail-oriented Program Analyst to support Generative AI initiatives focused on prompt engineering, feature validation, test execution, output quality evaluation , and program tracking . The role will support internal GenAI platforms and tools, including search, summarization, RAG-based workflows, analytical agents, and domain-specific AI assistants.
The ideal candidate should combine strong program coordination skills with a practical understanding of GenAI systems, structured prompt-based testing, multi-source data validation, and clear reporting for engineering and leadership stakeholders.
Key Responsibilities
1. GenAI Program Tracking & Coordination
- Track GenAI feature validation plans, deliverables, owners, dependencies, and closure status across teams.
- Maintain dashboards, trackers, action item logs, risk registers, and validation status reports.
- Coordinate with engineering, product, platform, and domain expert teams to drive timely completion of testing and feedback cycles.
- Consolidate weekly progress updates, blockers, risks, and recommendations for program reviews.
- Ensure validation documentation, prompt libraries, and issue logs are accurate, current, and accessible.
2. Prompt Engineering & Prompt Test Design
- Create, refine, and maintain prompts for GenAI features, search workflows, summarization use cases, and analytical agents.
- Develop zero-shot, few-shot, role-based, multi-turn, edge-case, and negative test prompts.
- Build reusable prompt templates and feature-wise prompt libraries.
- Compare prompt variants and document impact on accuracy, relevance, completeness, consistency, and latency.
- Translate domain expert feedback into improved prompts and updated test scenarios.
3. Feature Validation & Test Execution
- Design and execute structured test cases for GenAI features, including functional, sanity, regression, negative, and exploratory testing.
- Validate end-to-end behavior from user prompt to retrieved context, generated response, and final output format.
- Map feature requirements to prompts, expected outputs, acceptance criteria, and validation evidence.
- Identify defects, usability gaps, hallucinations, incomplete answers, inconsistent responses, and low-confidence outputs.
- Support release readiness assessment using clear status indicators such as Go / Watch / Risk.
4. Data, Search & RAG Validation
- Validate AI outputs against structured, semi-structured, and unstructured data sources such as SharePoint, Confluence, Jira, documents, JSON/XML, and internal knowledge bases.
- Check search relevance, context quality, source completeness, and data freshness before summarization.
- Support validation of RAG-based workflows, semantic search, keyword search, global filters, and domain filters.
- Identify data gaps, indexing issues, retrieval failures, and source-quality problems impacting AI response accuracy.
- Maintain golden datasets, expected-answer sets, and benchmark queries for repeatable validation.
5. Output Quality Evaluation & Defect Analysis
- Evaluate GenAI responses for factual correctness, relevance, completeness, clarity, format quality, and consistency across repeated runs.
- Log defects with clear reproduction steps, prompt used, expected output, actual output, severity, and owner.
- Perform first-level root cause analysis to distinguish prompt issues, data issues, retrieval issues, model limitations, and product defects.
- Work with SMEs to validate technical correctness and define acceptance thresholds for complex responses.
- Track quality improvements over time and report trends in accuracy, hallucination reduction, and feature readiness.
6. Reporting, Documentation & Stakeholder Communication
- Prepare weekly validation summaries, risk updates, action item trackers, and executive-ready status reports.
- Document prompt libraries, test suites, validation evidence, known issues, and improvement logs.
- Facilitate review meetings, capture minutes of meeting, document decisions, and follow up on action items.
- Communicate clearly with cross-functional stakeholders across engineering, platform, product, and program management teams.
- Support leadership reviews with concise summaries, dashboards, and recommendations.
7. Automation & Productivity Support
- Use Excel, Power BI, Jira, Confluence, SharePoint, or similar tools to maintain trackers, dashboards, and reporting views.
- Support basic scripting or automation for batch prompt execution, response capture, comparison, and reporting where applicable.
- Help improve validation efficiency through reusable templates, structured checklists, and repeatable test processes.
- Track productivity metrics such as test coverage, closure rate, defect aging, prompt reuse, and feature readiness.
Required Skills & Competencies
- 2–6 years of experience in program analysis, project coordination, validation, QA, data analysis, or technical operations.
- Hands-on exposure to Generative AI tools, LLM-based applications, prompt engineering, or AI-assisted workflows.
- Strong ability to create structured test cases, validation reports, action trackers, and executive summaries.
- Good understanding of prompt-response behavior, hallucinations, context limitations, and output evaluation methods.
- Working knowledge of search, summarization, RAG concepts, information retrieval, or knowledge-base validation.
- Strong Excel skills; Power BI, Jira, Confluence, SharePoint, or dashboarding experience is preferred.
- Basic Python, SQL, or scripting knowledge is preferred but not mandatory.
- Excellent written and verbal communication skills with strong attention to detail.
Preferred Qualifications
Education & Background
- Bachelor’s degree in Engineering, Computer Science, Electronics, Data Science, Information Systems, or a related discipline.
- Prior experience in semiconductor, SoC, embedded systems, platform engineering, enterprise AI, or technical program management is preferred.
- Experience supporting validation of internal tools, AI platforms, search systems, summarization tools, or workflow automation platforms is a plus.
- Exposure to responsible AI, data privacy, model evaluation, or enterprise GenAI governance is desirable.
Success Metrics
- Completeness and accuracy of GenAI feature validation coverage.
- Quality, reusability, and effectiveness of prompt test suites.
- Timely closure of validation defects, risks, action items, and stakeholder feedback.
- Improvement in output accuracy, relevance, consistency, and hallucination reduction.
- Clarity and usefulness of weekly reports, dashboards, executive summaries, and readiness recommendations.
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