Process Nine Technologies - Lead Data Scientist - Machine Learning/Artificial Intelligence

Lead Data Scientist (Multilingual AI/ML)

Location : Gurgaon, Haryana

Job Summary

We are seeking an experienced and visionary Lead Data Scientist to spearhead our multilingual Artificial Intelligence and Machine Learning initiatives. This pivotal role focuses on advanced natural language processing, cutting-edge speech technologies, and the development of highly specialized, domain-specific AI/ML models. The ideal candidate will possess extensive hands-on experience in training and fine-tuning state-of-the-art transformer-based models, developing robust speech recognition and synthesis systems, and adapting large language models for complex, specialized applications. You will provide technical leadership, drive innovation, and mentor a talented team to deliver high-impact AI solutions.

Job Responsibilities

• Lead the end-to-end development of multilingual transformer models, with a specific focus on applications such as machine translation using frameworks like OpenNMT, and cutting-edge Text-to-Speech (TTS) and Speech-to-Text (STT) systems leveraging tools like Coqui TTS.
• Conduct advanced fine-tuning of Whisper models for highly accurate and specialized speech-to-text transcription for diverse use cases and accents.
• Perform sophisticated fine-tuning of Large Language Models (LLMs) to adapt them for specific domains, particularly within the fintech sector, or other specialized business applications.
• Architect and build Agentic chatbots and complex conversational AI systems using advanced orchestration frameworks such as LangChain and LlamaIndex.
• Design and implement robust synthetic data pipelines to generate high-quality training data, particularly for low-resource languages or specialized domains.
• Develop and execute cross-lingual training strategies to enhance model performance and generalization across multiple languages.
• Mentor and guide junior and mid-level data scientists and ML engineers, fostering a culture of technical excellence, continuous learning, and best practices in AI/ML development.
• Collaborate closely with engineering teams to ensure efficient and scalable model deployment, monitoring, and MLOps practices in production environments.
• Stay abreast of the latest research and advancements in multilingual NLP, speech AI, and LLM technologies, integrating innovative approaches into our product strategy.
  
  

Desired Profile & Required Skills And Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Computational Linguistics, Statistics, or a related quantitative field.
• 5+ years of progressive experience in Machine Learning and Natural Language Processing (ML/NLP), with a proven track record of successful project delivery.
• 2+ years of demonstrated leadership experience in a data science or ML engineering team, including mentoring and guiding technical teams.
• Strong proficiency in Python for data manipulation, model development, and scripting.
• Expertise with leading deep learning frameworks such as PyTorch and/or TensorFlow.
• In-depth hands-on experience with the Hugging Face ecosystem, including Transformers, Tokenizers, and Datasets libraries.
• Proven experience with MLOps practices and principles, including model versioning, pipeline automation, and monitoring.
• Familiarity with containerization technologies such as Docker and orchestration platforms like Kubernetes for scalable ML deployments.
• Experience working with major cloud platforms (AWS, Google Cloud Platform, or Azure) and their respective AI/ML services.
• Strong understanding of data privacy and compliance requirements, especially within sensitive contexts like the fintech industry.
• Excellent analytical, problem-solving, and communication skills, with the ability to articulate complex technical concepts to diverse stakeholders.
  
  

Preferred Skills

• Experience with other multilingual NLP toolkits or platforms.
• Familiarity with distributed training of large models.
• Publications in relevant AI/ML conferences or journals.
• Experience with other speech AI frameworks or technologies.
• Knowledge of graph databases or knowledge graph construction.
  
  

(ref:hirist.tech)