Senior ML - GenAI Engineer, Voice & Speech

Accountabilities:

• • Design and develop machine learning infrastructure, tooling, models, and platforms that enable teams to deliver high-quality AI-powered products.
  • Build internal products and platforms that make it easier for engineering teams to incorporate AI and Generative AI capabilities into customer-facing features.
  • Partner with product and engineering teams to explain machine learning data lifecycles, experimentation requirements, common patterns, anti-patterns, and technical tradeoffs.
  • Consult with teams on end-to-end AI product development, helping them design effective and responsible customer experiences.
  • Build scalable and resilient services supporting data integration, event processing, distributed workloads, and platform extensions.
  • Contribute to product functionality capable of processing large amounts of data and traffic reliably and efficiently.
  • Develop high-quality, performant, maintainable, sustainable, and testable code while taking ownership of engineering quality.
  • Work across distributed cloud components and services to deliver robust machine learning solutions.
  • Collaborate with stakeholders to translate product objectives into actionable engineering strategies and implementation plans.
  • Develop and deploy machine learning models and pipelines using modern LLM, RAG, prompt engineering, fine-tuning, evaluation, and multimodal approaches.
  • Build and operate low-latency natural language and real-time audio systems at scale, including transcription, ASR, interruption detection, audio alignment, and speech synthesis.
  • Develop and maintain reliable libraries, SDKs, APIs, and abstractions for internal engineering teams.
  • Work with large-scale datasets, including systems handling terabytes of data and hundreds of millions to billions of records.
  • Coach and mentor engineers, share expertise, encourage best practices, and contribute to a collaborative technical culture.
  • Explore emerging AI technologies and contribute to rapid prototyping and innovative solutions for evolving, open-ended problems.

  Requirements:

  • 5+ years of professional experience in Machine Learning or AI, preferably with a focus on natural language, plus strong software engineering and systems experience.
  • Proven experience building and deploying ML-driven B2B, multi-tenant applications in production environments at significant scale.
  • Experience managing and processing terabytes of data or hundreds of millions to billions of records.
  • Strong programming skills in Python and experience with modern ML technologies and tooling such as Jupyter, Dagster, MLFlow, KubeFlow, DVC, Triton Server, LLMs, and Postgres.
  • Hands-on experience with LLMs, RAG, prompt engineering, fine-tuning, LLM evaluation, and multimodal models.
  • Experience with data labeling or annotation for audio or text-based machine learning use cases.
  • Strong understanding of distributed systems and experience designing scalable, redundant, observable, and resilient services.
  • Expertise in designing systems that operate across distributed datasets and services.
  • Experience building and deploying solutions on public cloud platforms such as AWS or GCP.
  • Strong engineering background with at least 3 years of experience in software engineering and systems, including coding and system design.
  • Experience developing low-latency natural language models and pipelines at scale.
  • Hands-on experience with real-time audio and voice technologies, including transcription, ASR pipelines, interruption detection, audio alignment, and speech synthesis.
  • Familiarity with emerging AI technologies such as Model Context Protocol (MCP).
  • Proficiency with containers, orchestration, and large-scale deployment patterns; experience with Kubernetes or GKE and the Operator Pattern is a plus.
  • Experience working with highly sensitive data such as PHI/HIPAA and PII.
  • Familiarity with automation and container-based workflow engines, GitOps, infrastructure as code, and configuration-driven systems.
  • Experience creating clean abstractions, intuitive APIs, stable libraries, and reusable SDKs.
  • Demonstrated ability to deliver complex projects on time in enterprise-grade production environments.
  • Strong leadership, mentorship, collaboration, communication, and stakeholder-management skills.
  • Self-driven mindset, strong bias for action, strategic thinking, technical curiosity, and a passion for execution.
  • Bachelor's or equivalent advanced technical education in Computer Science, Machine Learning, Engineering, Data Science, or a related field is advantageous.
  • A preference for open-source technologies, greenfield development, rapid prototyping, and solving ambiguous, continuously evolving problems.

  Benefits:

  • Fully remote opportunity based in India.
  • Opportunity to work on advanced Generative AI, machine learning, voice, speech, and natural language technologies.
  • Exposure to large-scale systems processing hundreds of millions of users and extensive datasets.
  • Hands-on work with modern AI technologies including LLMs, RAG, multimodal models, fine-tuning, LLM evaluation, and real-time audio pipelines.
  • Opportunity to influence how AI capabilities are integrated into customer-facing products and internal engineering platforms.
  • High degree of autonomy within a cross-functional, self-empowered Agile environment.
  • Collaboration with highly skilled engineers, product professionals, and technical stakeholders.
  • Opportunities to mentor others and contribute to engineering standards, architecture, and technical strategy.
  • Exposure to distributed cloud infrastructure, Kubernetes, workflow automation, observability, GitOps, and infrastructure-as-code practices.
  • Opportunity to work in a greenfield environment with rapid prototyping and open-ended technical challenges.
  • Strong focus on continuous learning, experimentation, innovation, and advancing practical AI capabilities.
  • Flexible remote setup with an expectation to overlap India and U.S. business hours.