3 Torchscript Jobs

We are looking for a highly experienced Voice AI /ML Engineer to take the lead in designing and deploying real-time voice intelligence systems. This position specifically involves working on ASR, TTS, speaker diarization, wake word detection, and developing production-grade modular audio processing pipelines to support next-generation contact center solutions, intelligent voice agents, and high-quality audio systems. You will be operating at the convergence of deep learning, streaming infrastructure, and speech/NLP technology, with a focus on creating scalable, low-latency systems that cater to diverse audio formats and real-world applications. Your responsibilities will include: - Building, fine-tuning, and deploying ASR models such as Whisper, wav2vec2.0, and Conformer for real-time transcription. - Developing high-quality TTS systems using VITS, Tacotron, FastSpeech for natural-sounding voice generation. - Implementing speaker diarization to segment and identify speakers in multi-party conversations using embeddings and clustering techniques. - Designing wake word detection models with ultra-low latency and high accuracy even in noisy conditions. In addition to the above, you will also be involved in: - Architecting bi-directional real-time audio streaming pipelines utilizing WebSocket, gRPC, Twilio Media Streams, or WebRTC. - Integrating voice AI models into live voice agent solutions, IVR automation, and AI contact center platforms. - Building scalable microservices for audio processing, encoding, and streaming across various codecs and containers. - Leveraging deep learning and NLP techniques for speech and language tasks. Furthermore, you will be responsible for: - Developing reusable modules for different voice tasks and system components. - Designing APIs and interfaces for orchestrating voice tasks across multi-stage pipelines. - Writing efficient Python code, optimizing models for real-time inference, and deploying them on cloud platforms. Join us to be part of impactful work, tremendous growth opportunities, and an innovative environment at Tanla, where diversity is championed and inclusivity is valued.,

As a Machine Learning Engineer at our company, you will be utilizing your expertise in Computer Vision, Natural Language Processing (NLP), and Backend Development. Your primary responsibilities will include developing ML/DL models for Computer Vision tasks such as classification, object detection, and segmentation, as well as for NLP tasks like text classification, Named Entity Recognition (NER), and summarization. You will also be implementing research papers and creating production-ready prototypes. To excel in this role, you must have a solid understanding of Machine Learning and Deep Learning concepts. Proficiency in tools and libraries like PyTorch, OpenCV, Pillow, TorchVision, and Transformers is essential. You will be optimizing models using techniques such as quantization, pruning, ONNX export, and TorchScript. Moreover, you will be tasked with building and deploying RESTful APIs using FastAPI, Flask, or Django, and containerizing applications using Docker for deployment on cloud or local servers. Your role will also involve writing clean, efficient, and scalable code for backend and ML pipelines. Strong backend skills using FastAPI, Flask, or Django are required, along with experience in utilizing NLP libraries like Hugging Face Transformers, spaCy, and NLTK. Familiarity with Docker, Git, and Linux environments is crucial for this position, as well as experience with model deployment and optimization tools such as ONNX and TorchScript. While not mandatory, it would be advantageous to have knowledge of Generative AI / Large Language Models (LLMs) and experience with MLOps tools like MLflow, DVC, and Airflow. Additionally, familiarity with cloud platforms such as AWS, GCP, or Azure would be a plus. The ideal candidate should hold a Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. This is a full-time position with an evening shift schedule from Monday to Friday. The work location is in person.,

As an AI Engineer, you will have the exciting opportunity to work with companies seeking individuals who are passionate and hands-on in developing and scaling intelligent features directly into their products. In this fast-paced and high-impact role, you will be instrumental in designing machine learning pipelines, fine-tuning models, and seamlessly deploying them. Your responsibilities will include designing, training, and deploying machine learning and deep learning models, such as NLP, vision, or tabular models. You will collaborate closely with product and engineering teams to build end-to-end AI-driven features. Additionally, you will be responsible for building and maintaining data pipelines, monitoring model performance in production environments, researching and implementing cutting-edge techniques to enhance model outcomes, optimizing models for performance and scalability, and ensuring reproducibility and version control of model experiments. To excel in this role, you should have a strong foundation in machine learning and deep learning algorithms. Proficiency in Python and ML libraries like PyTorch, TensorFlow, and Scikit-learn is essential. Experience with model deployment and serving, including REST APIs, ONNX, and TorchScript, will be beneficial. Familiarity with data handling tools such as Pandas and NumPy, as well as workflow tools like MLflow and Airflow, is also required. Strong problem-solving skills and an iterative, experiment-driven mindset are key attributes for success in this position. In terms of qualifications, a minimum of 3-10 years of relevant experience is required. Exposure to additional areas such as LLMs, embeddings, vector databases (e.g., FAISS, Pinecone), MLOps or DevOps workflows, GPT-like models, retrieval-augmented generation (RAG), multimodal systems, cloud platforms (AWS, GCP, or Azure), and streaming data or real-time systems will be considered a bonus. Joining this role offers you high ownership and the opportunity to shape AI-first product experiences. You can expect a fast-paced learning environment with exposure to the entire product lifecycle. The collaborative team setting provides room for growth and leadership opportunities, allowing you to work on cutting-edge ML applications with significant real-world user impact.,