Senior AI Scientist

Key Responsibilities:

• Algorithm Development: Conceptualize and develop novel machine learning and deep learning models to solve complex business problems in image, video,time series processing and must have experience in LLM.

• On-Device Algorithm Design: Architect and train resource-aware machine learning models for edge applications including Human Activity Recognition (HAR), low power vision, and localized speech-to-text

• Model Optimization: Improve model accuracy, scalability, and inference latency using data-efficient fine-tuning and optimization techniques.

• Local-First GenAI & RAG: Pioneer privacy-preserving, low-latency deployment of local LLM orchestration and localized Retrieval-Augmented Generation (RAG) platforms

• Full-Stack AI Prototyping: Build end-to-end AI product pipelines, developing everything from custom CLI developer tools and embedded C/C++ logic to asynchronous backend architectures (PostgreSQL, Redis, BullMQ)

• Research to Production: Collaborate with engineering and DevOPS/MLOps teams to deploy, monitor, and maintain AI systems in production environments.

• Technical Leadership: Mentor junior data scientists and researchers, fostering a culture of innovation, rigorous scientific experimentation, and continuous learning to build a product.

• Cross-Functional Collaboration: Translate complex AI methodologies into actionable business strategies and effectively communicate results to non technical stakeholders.

• Industry Leadership: Stay abreast of the latest AI trends, publish research findings, and contribute to the broader scientific and open-source communities.

Domain Experience:

• Proven track record in genetic multi-objective optimization for model pruning or custom NAS tools.

• Deep understanding of human-computer interaction, wearable sensors (IMU data processing), or edge-based camera systems.

• Experience with multi-provider LLM orchestration, structured document extraction, or automated evaluation pipelines.

Qualifications & Skills

Education: Advanced degree (Ph.D. or Master’s) in Computer Science, Artificial Intelligence, Statistics, or a highly quantitative field from a top tier University of India.

Technical Stack:

• Languages: Advanced proficiency in Python.

• Frameworks: Hands-on experience with deep learning libraries such as TensorFlow, PyTorch, and Keras.

• Experience: 5+ years of relevant industry or research experience designing, training, and validating ML algorithms.

• Core Competencies: Strong foundational knowledge of probability, statistics, linear algebra, and experimental design.

• Track Record: Proven ability to transition ML prototypes into scalable production pipelines, backed by a portfolio of successful AI/data projects.

What We Offer

• A highly collaborative, research-driven environment with opportunities to publish work and attend top-tier AI conferences.

• Competitive base salary, performance bonuses, and continuous learning/development stipends.

• Flexible working arrangements and state-of-the-art computational resources.