Location Bengaluru Employment Type Full time Location Type On-site Department Product R D About G2 - The Company When you join G2 you re joining the team that helps businesses reach their peak potential by powering decisions and strategies with trusted insights from real software users G2 is the world s largest and most trusted software marketplace More than 100 million people annually including employees at all Fortune 500 companies use G2 to make smarter software decisions based on authentic peer reviews Thousands of software and services companies of all sizes partner with G2 to build their reputation and grow their business including Salesforce HubSpot Zoom and Adobe To learn more about where you go for software visit www g2 com and follow us on LinkedIn As we continue on our growth journey we are striving to be the most trusted data source in the age of AI for informing software buying decisions and go-to-market strategies Does that sound exciting to you Come join us as we try to reach our next PEAK About G2 - Our People At G2 we have big goals but we stay grounded in our PEAK Performance Entrepreneurship Authenticity Kindness values You ll be part of a value-driven growing global community that climbs PEAKs together We cheer for each other s successes learn from our mistakes and support and lean on one another during challenging times With ambition and entrepreneurial spirit we push each other to take on challenging work which will help us all to grow and learn You will be part of a global diverse team of smart dedicated and kind individuals - each with unique talents aspirations and life experiences At the heart of our community and culture are our people-led ERGs which celebrate and highlight the diverse identities of our global team As an organization we are intentional about our DEI and philanthropic work like our G2 Gives program because it encourages us all to be better people About The Role G2 is looking for a Senior Full-Stack AI Engineer with strong production experience across modern web backend systems and hands-on exposure to LLMs Voice AI and AI data platforms You ll lead end-to-end execution across the stack designing reliable services building real-time conversational experiences and owning the data and evaluation foundations that turn large volumes of interview interactions into structured insights and continuously improving models This role is ideal for someone who can balance product velocity with engineering rigor and who enjoys working across voice pipelines retrieval agent workflows and data evaluation systems to deliver measurable quality improvements over time In This Role You Will LMM Agent Development Prompting RAG Evaluation Lead prompt design and iteration for summarisation decision-making multi-turn dialogue agent behaviours and tool function calling Build and maintain evaluation harnesses golden sets rubrics regression suites to measure accuracy consistency safety and usefulness across releases Implement and optimize RAG Retrieval-Augmented Generation workflows chunking strategies embeddings retrieval reranking citations and grounding techniques to reduce hallucinations Define strategies for knowledge freshness and context management across a project s lifecycle e g project-specific knowledge bases interview-derived artifacts evolving taxonomies Voice AI Real-Time Conversational Systems Integrate and optimize components in AI-powered voice pipelines STT NLU TTS turn-taking barge-in interrupt handling session state Improve multi-turn voice experience quality latency timing alignment disfluency handling and context retention Build voice simulation and test tooling to validate real-world and adversarial scenarios noise accents interruptions partial transcripts Partner with ML Voice specialists to diagnose ASR misfires timing mismatches and agent voice orchestration issues AI Data Platforms ETL ELT Information Extraction Reporting Datasets Design ingestion and transformation workflows for high-volume interview data audio transcripts free-text responses metadata annotations Build ETL ELT pipelines that validate normalize inputs run information extraction entities themes taxonomy labeling key moments and produce curated queryable reporting datasets Establish data models and schemas that preserve lineage from raw sources intermediate artifacts curated outputs report-ready datasets Implement data quality practices completeness validity checks sampling-based verification reconciliation and monitoring for drift Build mechanisms for traceability and auditability e g linking report outputs back to transcript spans timecodes retrieval sources and model prompt versions Continuous Improvement Fine-Tuning Adaptation Learning Over a Project Collaborate with ML Data teams to support fine-tuning and or model adaptation workflows dataset curation labeling guidelines training eval splits offline evaluation rollout validation Implement project-level feedback loops so the system improves as more interviews occur - maintain evolving taxonomies and question strategies - incorporate newly discovered concepts into retrieval stores - update prompts policies based on failure patterns - expand evaluation sets automatically with new edge cases Build mechanisms for real-time or iterative learning without sacrificing safety e g controlled updates to RAG indexes prompt version rollouts gated releases human review where needed Enable the agent to ask more intelligent follow-up questions by using accumulated project knowledge grounded in retrieved evidence and governed by safety policies Backend Engineering Architecture Reliability Observability Own architecture and implementation of backend services and workflows supporting LLM voice data experiences APIs orchestration storage queues Improve system resilience through observability tracing structured logging rate limiting fallbacks and failure-mode design Lead debugging and resolution of complex issues across LLM pipelines retrieval systems data workflows and conversational agent logic Build internal tools to accelerate diagnosis QA and safe experimentation Automated Testing Security Quality Engineering Design and maintain automated test suites for APIs pipelines RAG systems and LLM outputs regression reliability performance load Use LLMs to generate synthetic datasets for robust coverage across realistic and adversarial conditions Establish quality gates in CI CD eval thresholds golden tests contract tests to ensure safe deployments Proactively identify and mitigate threats such as prompt injection data leakage and abuse scenarios Technical Leadership Collaboration Lead projects end-to-end requirements shaping technical design implementation rollout monitoring and iteration Mentor engineers through code reviews pairing design guidance and raising engineering standards Communicate tradeoffs clearly with stakeholders influence roadmap decisions through technical insight Contribute to documentation runbooks and best practices for production-grade AI systems Minimum Qualifications We realize applying for jobs can feel daunting at times Even if you don t check all the boxes in the job description we encourage you to apply anyway Required 5-8 years of professional software engineering experience full-stack backend platform or data-adjacent systems Hands-on experience with LLMs e g OpenAI Anthropic Claude Mistral etc including prompt design and evaluation Experience implementing or operating RAG systems embeddings retrieval reranking grounding citations Strong proficiency in Python and or JavaScript TypeScript with production experience Experience designing and operating reliable services APIs background jobs event-driven workflows Experience with automated testing frameworks and CI CD strong engineering rigor and ownership mindset Experience building ETL ELT workflows data transformations and report-ready datasets from semi-structured unstructured inputs Familiarity with real-time voice systems conversational agents or low-latency interactive products Preferred FastAPI or similar for building and scaling REST services excellent Python fundamentals Next js React for rapid prototyping and UI validation of conversational experiences Experience with observability stacks metrics tracing performance tuning and incident response practices Experience supporting fine-tuning model adaptation workflows dataset curation labeling eval rollout Experience with security considerations for AI products prompt injection defense data leakage prevention abuse monitoring Our Commitment to Inclusivity and Diversity At G2 we are committed to creating an inclusive and diverse setting where people of every background can thrive and feel welcome We consider applicants without regard to race color creed religion national origin genetic information gender identity or expression sexual orientation pregnancy age or marital veteran or physical or mental disability status - How We Use AI Technology in Our Hiring Process G2 incorporates AI-powered technology to enhance our candidate evaluation process These tools may assist with initial application screening skills assessment analysis and identifying candidates whose qualifications align with specific role requirements While AI technology supports our recruitment workflow all final hiring decisions remain under human oversight and judgment Your Choice Matters If you would prefer that your application be reviewed without AI assistance you can opt out by entering your email address in the email entry field at the bottom of the Automated Processing Legal Notice Choosing to opt out will not disadvantage your application in any way we will ensure your materials receive a thorough manual review by our hiring team For additional details about how we handle your information throughout the application process please review G2 s Applicant Privacy Notice

📌 Senior Ai Engineer (Karnataka)
🏢 G2
📍 Karnataka