Founding Engineer (Machine Learning, Data Science)

JOB DESCRIPTION Experience : 7 - 12 Yrs Location : Bengaluru Designation : Founding Engineer (Machine Learning, Data Science) The Role: We're looking for a talented Founding Engineer to lead the development and deployment of AI/ML models. You'll be responsible for the full pipeline—from building and fine-tuning models to implementing scalable workflows and deploying production-grade AI solutions. As part of a small and dynamic team, your contributions will be instrumental in defining our AI/ML strategy. Additionally, you'll have the chance to mentor and shape the future of our growing data science team. Key Responsibilities: • Machine Learning Model Development: Design, build, and deploy cutting-edge ML models across supervised/unsupervised learning, deep learning, and reinforcement learning. • Agentic Workflows Implementation: Develop autonomous AI-driven workflows to enhance operational efficiency. • Data Infrastructure: Architect and manage scalable data pipelines to handle structured and unstructured data. • Model Optimization: Fine-tune pre-trained models and implement models optimized for real-world applications in pharma and materials. • Collaboration: Work closely with cross-functional teams (product, engineering) to integrate AI solutions into business workflows. • Mentorship: Help guide and mentor junior data scientists, setting technical standards for the team. Top Requirements: • Experience: 8+ years in data science, preferably within pharmaceutical or highgrowth tech sectors (e.g., fintech, healthcare, or similar). • Technical Proficiency: Expertise in Python, SQL, and machine learning frameworks (TensorFlow, PyTorch, Scikit-learn). • Data Engineering Skills: Experience with tools like Airflow, Spark, or dbt for data pipelines and workflows. • Cloud Platforms: Hands-on experience with cloud platforms (AWS, GCP, or Azure) for data storage and model deployment. • Model Fine-Tuning: Expertise in fine-tuning ML models for specific tasks and ensuring optimal performance. • Problem Solving: Strong analytical and problem-solving abilities with a focus on innovative solutions. • Big Data: Knowledge of big data technologies (Hadoop, Spark) and ETL/ELT pipelines. • Agentic Workflows: Experience designing and implementing agentic workflows that leverage AI agents for automation. • Statistical Analysis: Strong foundation in statistical methods and machine learning algorithms. Bonus Skills: • Startup Experience: Background in startup environments or high-growth companies. • MLOps: Familiarity with MLOps best practices. • Containerization & Orchestration: Experience with Docker, Kubernetes, etc. • Data Visualization: Proficiency in tools like Tableau or Power BI for presenting data insights.