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.