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 LLMs 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 Parspecs 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

• Bachelors or Masters 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 LLMs, 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.