Job Title: Staff Engineer / Sr. Mgr - AI - Engineer (Voice Applications)

Location: Chennai The Opportunity:

We are seeking a seasoned and visionary AI - Engineer to join our core AI/ML team, specifically focused on architecting and leading the development of cutting-edge Voice AI applications.

As the AI Lead Engineer for Voice Applications, you will play a pivotal role in designing, developing, and operationalizing advanced AI/ML solutions that directly enable high-accuracy Speech Transcription (STT) and real-time Audio Translation services. This role is ideal for a hands-on technical leader who thrives at the intersection of robust engineering and innovative voice AI research.

You will lead the end-to-end design and deployment of scalable, low-latency Voice AI systems, transforming state-of-the-art models (such as Transformer/Attention-based architectures) into robust, production-grade solutions. Your work will directly influence mission-critical operations involving multilingual communication and large-scale audio data processing.

Key Responsibilities:

Voice AI System Architecture:

- Lead the design and implementation of scalable, secure, and high-performance Voice AI architectures, specifically optimized for high-throughput audio transcription and translation services.
- Define architectural standards and best practices for audio data pipelines, model deployment, and system integration in a voice-centric context.

End-to-End Voice Solution Delivery:

- Own the full lifecycle of Voice AI solutionsfrom ideation and prototyping to deployment, monitoring, and continuous improvement.
- Ensure solutions are production-ready, maintainable, and meet stringent requirements for accuracy, latency, and real-time performance across multiple languages and dialects.

Engineering Leadership:

- Guide and mentor a team of AI/ML engineers, fostering a culture of technical excellence, innovation, and collaboration. Conduct code reviews, set engineering standards, and drive continuous improvement within the Voice AI practice.

Cloud-Native AI:

- Leverage and integrate cloud-native AI/ML services (AWS, GCP, Azure),



including specialized Speech and Translation APIs, to accelerate development and ensure scalability.
- Optimize compute, storage, and networking for intensive audio processing and real-time inference workloads.

MLOps & Automation for Voice:

- Champion MLOps practices, including CI/CD for ML, model versioning, automated testing, and retraining pipelines.
- Implement robust monitoring for operational reliability and model performance, with specific attention to audio data drift, acoustic quality, and transcription/translation error rates.

Cross-Functional Collaboration:

- Partner with data scientists, researchers, product managers, and business stakeholders to translate complex voice requirements into scalable and business-aligned AI/ML solutions.

Innovation & Problem Solving:

- Stay ahead of the curve on emerging technologies in Speech Recognition, Neural Machine Translation (NMT), and large-scale audio processing. Identify opportunities to apply novel techniques (e.g., few-shot learning, cross-lingual transfer) to solve high-impact voice-related business problems.

Required Qualifications:

Education:

- M.Tech or PhD in Computer Science, Machine Learning, Computational Linguistics, or a closely related quantitative discipline from a top-tier institution.

Experience:

- Minimum 58 years of hands-on experience in AI/ML engineering, with a proven track record of delivering production-grade solutions at scale.

Proven Impact:

- Demonstrated success in designing, deploying, and maintaining multiple high-volume Voice AI systems (STT and/or NMT) in production. Be prepared to discuss the business impact, technical challenges, and your leadership role.

Technical Mastery (Voice Focus):

- Expert-level proficiency in Python and software engineering principles.




- Deep expertise in Voice AI/NLP/Audio frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers, Fairseq, NeMo, or equivalent).
- Solid understanding of modern Speech-to-Text (STT) and Neural Machine Translation (NMT) models (e.g., Whisper, Wav2Vec, Transformer architectures).
- Expertise in working with large audio datasets, acoustic modeling, and language modeling.

Cloud Expertise:

- Deep experience with at least one major cloud platform (AWS, Azure, GCP), particularly with AI/ML services, infrastructure automation, and cost optimization.

MLOps Proficiency:

- Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker Pipelines). Strong understanding of model lifecycle management and operational monitoring.

Preferred Qualifications:

- Experience with real-time audio streaming and processing technologies (e.g., Kafka, gRPC).
- Familiarity with containerization and orchestration (Docker, Kubernetes) for microservices supporting Voice AI.
- Experience with advanced audio preprocessing techniques, noise reduction, and handling various dialects/accents.
- Familiarity with distributed data processing and big data ecosystems for training large models (e.g., Spark, Databricks).

What We Offer:

- A leadership role at the forefront of Voice AI innovation, with direct influence on strategic, customer-facing initiatives.
- Access to cutting-edge tools, infrastructure, and large-scale audio datasets to support ambitious Speech and Translation projects.
- A collaborative, high-performance environment that values creativity, autonomy, and continuous learning.
- Competitive compensation and a clear path for career advancement within a rapidly growing AI organization.

Ready to Lead and Build Impact in Voice AI? If you're a hands-on AI/ML engineering leader with a passion for building intelligent systems that solve real-world communication problems through voice, we want to hear from you. Share your resume, highlight your most impactful projects, and tell us how you plan to shape the future of Voice AI at scale.

📌 AI Engineer (Voice Applications) (Chennai)
🏢 Durus consulting
📍 Chennai