AI Engineer - 12 LPA (3+ Years)

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About the Company

We are a rapidly growing AI-driven health-tech / biotech company building advanced artificial intelligence systems to transform drug discovery and development.

Role Overview

As an AI Engineer, you will work closely with AI/ML and scientific teams to design, develop and deploy machine-learning solutions for healthcare and drug-discovery applications.

You will be involved across the AI lifecycle — from data processing and model development to experimentation, evaluation, optimisation, and production deployment.

Key Responsibilities

• Design, develop and deploy machine learning and AI models for healthcare and life-sciences applications.
• Work with large-scale structured, unstructured and biological/multi-omics datasets.
• Develop predictive models for areas such as drug activity, efficacy, safety and biological outcomes.
• Build and optimise data preprocessing, feature engineering and model-training pipelines.
• Experiment with modern deep learning, NLP and generative AI techniques where appropriate.
• Evaluate model performance using appropriate statistical and ML metrics.
• Improve model accuracy, scalability, robustness and inference performance.
• Collaborate with data scientists, computational biologists and domain experts to translate scientific problems into ML solutions.
• Develop APIs and production-ready services around AI/ML models.
• Contribute to MLOps practices including model versioning, monitoring, testing and deployment.
• Stay current with advances in AI/ML, deep learning and AI applications in healthcare.

Required Skills

• 3–5 years of hands-on experience in AI/ML engineering or machine learning.
• Strong proficiency in Python.
• Experience with ML frameworks such as PyTorch, TensorFlow or scikit-learn.
• Strong understanding of:
• Machine learning algorithms
• Deep learning
• Model evaluation and optimization
• Data preprocessing and feature engineering
• Statistics and probability
• Experience working with SQL and data-processing pipelines.
• Experience building and deploying ML models in production.
• Familiarity with REST APIs, Git and cloud environments.
• Strong problem-solving and analytical skills.

Good to Have

• Experience with Generative AI / LLMs.
• Experience with NLP or transformer architectures.
• Exposure to healthcare, biotechnology, pharmaceuticals or computational biology.
• Experience working with multi-omics, genomic or biological datasets.
• Knowledge of cloud platforms such as AWS, GCP or Azure.
• Experience with Docker, Kubernetes or other containerization technologies.
• Familiarity with MLOps and model deployment at scale.