Smallest AI appoints Siddharth Sharma as President, BFSI, India
GlobeNewswire
Smallest AI appointed banking and digital-finance veteran Siddharth Sharma to lead revenue and partnerships for its growing BFSI business in India.
Read articlesmallest.ai is building next-generation AI infrastructure and voice technology to power intelligent products at scale. We work across speech, LLMs and real-time AI systems, building products and infrastructure that developers and businesses rely on.
We are looking for a hands-on Senior QA Automation & Performance Engineer to own quality engineering across our backend systems. You will design and build automation frameworks, perform functional and performance testing, and help ensure that our APIs and services remain reliable as traffic, customers and system complexity grow.
This is an individual-contributor role requiring strong technical ownership and close collaboration with engineering, product and infrastructure teams.
Own functional, integration, end-to-end and performance testing for backend services and APIs.
Design, build and maintain scalable automation frameworks rather than only adding test cases to an existing framework.
Automate testing across a multi-service, multi-tenant platform.
Validate tenant isolation, authentication, permissions, configuration differences, data integrity and concurrent tenant activity.
Design and execute load, stress, spike, scalability and endurance tests.
Define performance benchmarks and acceptance criteria for latency, throughput, concurrency, reliability and error rates.
Identify performance bottlenecks and work with backend and infrastructure engineers to resolve them.
Test business logic, data flows, asynchronous processes, integrations and complete customer workflows.
Build reusable test utilities, fixtures, test-data strategies and environment-management capabilities.
Integrate functional and performance test suites into CI/CD pipelines.
Analyse failures using logs, metrics and traces, and distinguish product defects from environment or test-framework issues.
Identify edge cases, regressions, failure scenarios and production risks before release.
Improve test coverage, quality gates and overall QA processes across the engineering organisation.
Collaborate effectively with engineers while independently driving testing priorities and execution.
4+ years of experience in software quality engineering, test automation or SDET roles.
Strong hands-on experience with both functional testing and performance testing.
Demonstrated experience building or significantly extending an automation framework from the ground up.
Experience testing backend APIs, distributed services and end-to-end workflows.
Experience working with multi-service architectures; exposure to multi-tenant SaaS systems is strongly preferred.
Hands-on experience with performance-testing tools such as k6, JMeter, Gatling or Locust.
Ability to design realistic workload models and analyse latency percentiles, throughput, concurrency, saturation and failure rates.
Strong programming or scripting skills in at least one language such as Python, Java, JavaScript/TypeScript or Go.
Experience integrating automated tests into CI/CD systems.
Working knowledge of databases, queues, caching, APIs and asynchronous workflows.
Familiarity with logs, monitoring and observability tools for troubleshooting test failures and performance bottlenecks.
Strong understanding of testing fundamentals, SDLC, STLC and defect-management practices.
Ability to operate as an independent contributor while working closely with multiple engineering teams.
Strong analytical thinking, attention to detail and clear written and verbal communication.
Experience in a high-growth or product-led technology startup.
Experience testing real-time, AI, voice, streaming or other latency-sensitive systems.
Familiarity with Kubernetes, Docker and cloud infrastructure.
Experience testing WebSockets, streaming APIs or event-driven architectures.
Experience with security, resilience, failover or chaos testing.
Experience validating SLA/SLO requirements and production performance metrics.
Build the testing and automation foundation for cutting-edge AI products.
Work on real-time, large-scale and latency-sensitive systems.
Own quality engineering across a complex, multi-service platform.
Work in a high-ownership environment with direct engineering impact.
Help the organisation ship faster while maintaining reliability and performance.
This Senior QA Automation & Performance Engineer role focuses on quality engineering for backend services and APIs. You will build automation frameworks, test system behavior and performance, and work with engineering, product, and infrastructure teams to improve reliability as platform usage grows.
Smallest AI is a foundational AI research and infrastructure company building real-time voice AI for enterprises. Its platform includes Lightning for text-to-speech, Pulse for speech-to-text, Electron as a small language model, Hydra for native speech-to-speech, and tools for deploying production voice agents. The company focuses on low-latency, multilingual speech systems for use cases including customer service, collections, onboarding and other contact-center workflows. It supports cloud, private-cloud and on-premise deployments for regulated enterprises and serves customers across sectors including financial services, healthcare, contact centers and BPO.
Smallest AI offers candidates the chance to work on technically demanding real-time AI problems spanning speech recognition, speech generation, speech-to-speech systems, small language models and production voice agents. The company raised a $13 million Series A in July 2026, taking total funding above $21 million, and is expanding enterprise deployments across regulated industries. Its engineering roles emphasize low latency, reliability, distributed systems, cloud infrastructure and high ownership. The company also develops proprietary AI models rather than only assembling third-party APIs, creating opportunities to work across research, infrastructure and production deployment.
Programming Language
Programming Language
Programming Language
Cloud Infrastructure
Container Orchestration
Containers
Infrastructure as Code
Kubernetes Tooling
Database
Cache and Data Store
Messaging
Messaging
Monitoring
Observability
Content Delivery Network
Application Platform
Co-founder and CEO of Smallest AI. An IIT Guwahati mechanical engineering graduate, Kamath has worked on AI and deep-learning products and previously held roles associated with Bosch, Toppr and Vakilsearch. At Smallest AI he leads the company and has been closely involved in developing its speech and voice AI products, including Lightning.
Co-founder of Smallest AI and an IIT Guwahati graduate. Mandloi has a technical background spanning AI, autonomous systems, deep learning and machine learning, including published work on perception-based navigation. At Smallest AI he works on the company's speech models and real-time voice AI technology.
Global Head of Go-To-Market at Smallest AI, responsible for commercial expansion and enterprise engagement. He previously served as Vice President of Global Business Development and Partnerships at WebEngage and has experience building partnerships and go-to-market programs for technology companies.
Chief Scientist at Smallest AI and an early investor and technical advisor to the company. Jain holds a PhD from the Max Planck Institute, has worked with Yann LeCun at NYU and has served as adjunct faculty at IISc. His research spans generative AI, computer vision, speech and multimodal systems, helping translate Smallest AI research into production.
President of BFSI, India at Smallest AI, responsible for revenue and partnerships across banking, financial services and insurance. He brings more than 20 years of financial-services experience and has led digital initiatives at organizations including ICICI Bank, YES BANK, Kotak Mahindra Bank and CRED.
GlobeNewswire
Smallest AI appointed banking and digital-finance veteran Siddharth Sharma to lead revenue and partnerships for its growing BFSI business in India.
Read articleTenstorrent
Smallest AI and Tenstorrent announced an on-premise voice AI stack running Smallest AI's Lightning V2 text-to-speech model on Tenstorrent Galaxy Blackhole servers, targeting lower inference costs and enterprise data sovereignty.
Read articlePR Newswire
Smallest AI raised a $13 million Series A led by Seligman Ventures with participation from Sierra Ventures and 3one4 Capital, bringing total funding to more than $21 million. The company also introduced its Voice 4.0 direction and Hydra speech-to-speech architecture.
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Smallest AIBengaluru, IndiaFull time
Smallest AIBengaluru, IndiaFull time