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 pipelinefrom 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.