Job Description

Work Flexibility: Hybrid or Onsite

• Design, develop, and deploy AI/ML-powered applications with a focus on speech, language, and GenAI use cases.
• Integrate and operationalize speech-to-text, text-to-speech, NLP, and LLM-based models into production-grade backend services.
• Build and maintain cloud-native AI pipelines on Azure, including model deployment, scaling, monitoring, and cost optimization.
• Develop AI-driven features such as:
  • Voice transcription and real-time speech processing
  • Intent detection and entity extraction
  • Conversational AI and virtual assistants
  • Summarization, semantic search, and RAG-based workflows
• Fine-tune and adapt models for domain-specific vocabulary, accents, noisy environments, and healthcare contexts.
• Collaborate closely with backend engineers, product owners, UX teams, and platform teams to deliver end-to-end AI features.
• Make independent architecture and design trade-off decisions across multiple components and services.
• Drive engineering best practices around testing, reliability, observability, and security for AI systems.
• Contribute to technical documentation, design reviews, and architecture discussions.
• Participate in customer-facing discussions (Voice of Customer) to align AI solutions with real-world usage and feedback.
• Actively contribute to innovation through patents, invention disclosures, or internal IP creation.
• Mentor junior engineers and raise the overall AI/ML engineering maturity of the team.

• Bachelor’s degree in Computer Science, Software Engineering, or a related field.
• 3-10 years of experience in software engineering with significant hands-on work in AI/ML systems.
• Strong proficiency in Python, with experience building production-grade ML or AI services.
• Solid understanding of machine learning, NLP, and modern AI model architectures, including LLMs.
• Experience deploying and operating AI workloads on Microsoft Azure.
• Strong engineering fundamentals: system design, APIs, data pipelines, scalability, and reliability.

• Hands-on experience with LLMs, NLP pipelines, RAG architectures, prompt engineering, and model evaluation.
• Experience integrating or fine-tuning speech models (ASR/TTS) for real-world applications.
• Familiarity with frameworks and tools such as Azure OpenAI, Azure ML, LangChain, MLflow, PyTorch, and TensorFlow.
• Exposure to sentiment analysis, intent recognition, semantic search, or conversational AI.

• Strong experience with Azure services, such as:
  • Azure OpenAI
  • Azure ML
  • Azure Functions / Container Apps
  • Azure AI Search
  • Storage, monitoring, and security services
• Experience designing cloud-native, scalable, and secure architectures.
• Familiarity with Docker, Kubernetes, CI/CD, and ML lifecycle management.
• Exposure to AWS or GCP is a plus.

• Experience integrating third-party APIs and AI services.
• Background in Java-based backend systems is a plus.
• Strong documentation, design review, and cross-team collaboration skills.
• Comfortable working in an Agile development environment.

Travel Percentage: 10%