Senior Machine Learning Engineer

Description:

A leading organization is seeking a Senior Machine Learning Engineer to develop advanced AI solutions for enterprise applications. This role is ideal for an experienced professional with expertise in machine learning, large language models (LLMs), and production-grade AI systems capable of solving complex business challenges.

The successful candidate will design, deploy, and optimize scalable machine learning solutions while working closely with cross-functional teams. The role focuses on building intelligent enterprise AI applications, Retrieval-Augmented Generation (RAG) architectures, semantic search capabilities, and secure on-premise deployments.

Key Responsibilities:

• Develop and fine-tune Large Language Models (LLMs) and NLP pipelines for enterprise applications.

• Design and implement Retrieval-Augmented Generation (RAG) architectures and semantic search solutions.

• Build forecasting, supplier risk-scoring, anomaly detection, and optimization models.

• Deploy, optimize, and maintain machine learning models in secure on-premise environments.

• Automate model training, deployment, and inference pipelines using MLOps practices.

• Monitor model performance, reduce inference latency, and continuously improve AI solutions.

• Collaborate with engineering and business teams to deliver production-ready machine learning systems.

Requirements:

• Minimum 5+ years of Machine Learning Engineering experience, with 7–10 years preferred.

• Strong programming skills in Python.

• Hands-on experience with PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.

• Experience working with Large Language Models (LLMs), Transformers, Hugging Face, LangChain, and RAG architectures.

• Proficiency in Docker, Kubernetes, MLflow, and production ML deployment.

• Experience handling structured and unstructured datasets, including SQL databases and technical documentation.

• Strong analytical, problem-solving, and cross-functional collaboration skills.

• Strong knowledge of:

    • Python, PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.

    • LLMs, NLP, Hugging Face, LangChain, and Retrieval-Augmented Generation (RAG).

    • Docker, Kubernetes, MLflow, SQL, and enterprise machine learning deployment.