Job description

About Us Deccan AI is a model training and eval startup headquartered in the Bay Area. We are founded by IIT Bombay, IIM Ahmedabad and ex-Google alumni, and work with some of the top AI frontier labs in the world, e.g. Google Deepmind, Snowflake, and many more. We are backed by Prosus Ventures, and our India delivery office is in Hyderabad. Most of our roles are on-site i.e. Bay Area / Hyderabad. About the Role We’re looking for a Founding ML Engineer to build and scale core machine learning systems from the ground up. This is a hands-on role for someone who can move fast between experimentation and deployment — blending research intuition with engineering execution. You’ll work closely with the founders to design, train, and ship production-grade ML models while setting the foundation for our technical culture, infrastructure, and research practices. What You’ll Do • Build and optimize end-to-end ML pipelines — from data ingestion to deployment. • Implement and fine-tune LLMs, embeddings, and generative models for real-world applications. • Develop efficient training and inference systems leveraging distributed compute. • Partner with the data and product teams to translate ideas into measurable ML impact. • Contribute to model monitoring, evaluation, and continual learning frameworks. • Establish best practices in model versioning, reproducibility, and scalability . What You’ll Bring • 3–7 years of experience as an ML Engineer / Applied Scientist / Research Engineer . • Proficiency in Python, PyTorch, TensorFlow, or JAX . • Strong grasp of ML fundamentals — data preprocessing, feature engineering, model training, and optimization. • Experience with distributed systems, cloud ML infra (AWS, GCP, or Azure) , and MLOps tools (Weights & Biases, MLflow, etc.). • Comfortable working with large datasets and high-throughput systems . • Bias for action, ability to work autonomously, and eagerness to shape something from scratch.

Requirements

Must-haves

You’ll work closely with the founders to design, train, and ship production-grade ML models while setting the foundation for our technical culture, infrastructure, and research practices.
What You’ll Do
Build and optimize end-to-end ML pipelines — from data ingestion to deployment.
Implement and fine-tune LLMs, embeddings, and generative models for real-world applications.
Develop efficient training and inference systems leveraging distributed compute.
Partner with the data and product teams to translate ideas into measurable ML impact.
Contribute to model monitoring, evaluation, and continual learning frameworks.
Establish best practices in model versioning, reproducibility, and scalability .
What You’ll Bring
3–7 years of experience as an ML Engineer / Applied Scientist / Research Engineer .
Proficiency in Python, PyTorch, TensorFlow, or JAX .
Strong grasp of ML fundamentals — data preprocessing, feature engineering, model training, and optimization.
Experience with distributed systems, cloud ML infra (AWS, GCP, or Azure) , and MLOps tools (Weights & Biases, MLflow, etc.).
Comfortable working with large datasets and high-throughput systems .
Bias for action, ability to work autonomously, and eagerness to shape something from scratch.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningpythonawsgcpazure

Platform: dover
Effort: medium