Senior Machine Learning Engineer

Title: Senior Machine Learning Engineer Location: Vaishnavi Signature, Bellandur, Bengaluru( hybrid2 days onsite a week) Full time What You Will Achieve and Key Responsibilities Research, Design, Develop and Deploy AI models and systems - Lead the research and development of AI models - a varied portfolio ranging from small classifiers to fine-tuning LLM’s for specific use-cases - Design, implement and deploy AI-based solutions to solve business and product problems - Develop and implement strategies to track and improve the performance and efficiency of existing and new AI models and systems - Operationalize efficient dataset creation and management - Execute best practices for end-to-end data and AI pipelines - Work closely with the leadership team on research and development efforts to explore cutting-edge technologies. - Collaborate with cross-functional teams including full-stack engineers, product managers, QA engineers, data annotation experts, SMEs and other stakeholders to ensure successful implementation of AI technologies Build and Mentor the AI Team - Work closely with the AI & Engineering Leadership to support hiring of top AI talent - Uphold our culture of engineering excellence by maintaining high standards in innovation & execution Why This Matters Your contributions will be instrumental in advancing Parspec’s AI capabilities, enabling us to build intelligent systems that solve real-world problems in construction technology. By developing scalable AI solutions, you will help digitize an industry while driving innovation through state-of-the-art machine learning techniques. Who You Are You are a motivated Machine Learning Engineer with at least 5 years of relevant experience who is passionate about working on innovative projects in a dynamic environment. You thrive on solving challenging problems using advanced AI technologies. Minimum Qualifications - Bachelor’s or Master’s degree in Science or Engineering with strong programming, data science, critical thinking, and analytical skills - 5+ years of experience building in ML and Data science - Recent demonstrable hand-on experience with LLMs - integrating off-the-shelf LLM’s, fine-tuning smaller models, building RAG pipelines, designing agentic flows, and other optimization techniques with LLMs - Strong conceptual understanding of foundational models, transformers, and related research - Strong conceptual understanding of the basics of machine learning and deep learning with expertise in Computer Vision and Natural Language Processing - Recent demonstrable experience with managing large datasets for AI projects - Experience with implementing AI projects in Python and working knowledge of associated Python libraries - numpy, scipy, pandas, sklearn, matplotlib, nltk, etc. - Experience with Hugging Face, Spacy, BERT, Tensorflow, Torch, OpenRouter, Modal, and similar services / frameworks - Ability to write clean, efficient, and bug-free code. - Proven ability to lead initiatives from concept to operation while navigating challenges effectively. - Strong analytical and problem-solving skills - Excellent communication and interpersonal skills Preferred Qualifications - Recent experience with implementing state-of-the-art scalable AI pipelines for extracting data from unstructured / semi-structured sources and converting it into structured information, along with necessary technical infrastructure to support deployment - Experience with cloud platforms (AWS, GCP, Azure), containerization (Kubernetes, ECS, etc.), and managed services like Bedrock, SageMaker, etc. - Experience with MLOps practices, e.G. model monitoring, feedback pipelines, CI/CD flows, and governance best-practices - Experience working with applications hosted on AWS or Django web frameworks. - Familiarity with databases and web application architecture. - Experience working with OCR tools or PDF processing libraries. - Completed academic or online specializations in Machine Learning or Deep Learning. - Track record of publishing research in top-tier conferences and journals - Participation in competitive programming (e.G., Kaggle competitions) or contributions to open-source projects. - Experience working with geographically distributed teams across multiple time zones. -