KKR - Artificial Intelligence/Machine Learning Engineer

Note : If shortlisted, you will be invited for initial rounds on 7th February 2026 (Saturday) in Gurugram

POSITION SUMMARY :

We are seeking an AI / ML Engineer to design, build, and deploy machine learning models and AI-driven solutions that solve real-world business problems at scale. You will work closely with data scientists, software engineers, and product teams to take models from experimentation to production.

KEY RESPONSIBILITIES :

- Design, develop, train, and deploy machine learning and AI models for production use

- Build and maintain end-to-end ML pipelines, including data ingestion, feature engineering, training, evaluation, and monitoring

- Collaborate with product and engineering teams to translate business requirements into ML solutions

- Implement scalable and reliable model-serving systems

- Evaluate and improve model performance, accuracy, fairness, and robustness

- Work with large, structured and unstructured datasets

- Conduct experimentation, A/B testing, and model validation

- Document models, assumptions, and technical decisions clearly

Qualifications Required :

- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field (or equivalent experience)

- Strong foundation in machine learning algorithms, statistics, and linear algebra

- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn

- Experience with data processing libraries (NumPy, Pandas, Spark, etc.)

- Understanding of supervised and unsupervised learning, model evaluation, and optimization techniques

- Experience deploying models via APIs or batch pipelines

- Solid software engineering fundamentals (version control, testing, code reviews)

Preferred Skillset :

- Experience with deep learning, NLP, computer vision, or recommender systems

- Hands-on experience with LLMs, prompt engineering, or fine-tuning foundation models

- Familiarity with MLOps practices (model monitoring, drift detection, retraining pipelines)

- Experience with cloud platforms (AWS, Azure, GCP) and managed ML services

- Knowledge of big data tools (Spark, Kafka)

- Understanding of data privacy, ethics, and responsible AI principles