AI / Machine Learning Engineer

About the Role

We are looking for a highly skilled AI/ML Engineer to design, build, and optimize machine learning systems that power real business applications. You will work with cross-functional teams to deliver scalable AI solutions including predictive models, NLP systems, recommendation engines, and automation tools.

This role is ideal for someone who loves solving complex problems with data, experimenting with new AI techniques, and taking models from prototype to production.

Key Responsibilities

Develop, train, and deploy machine learning and deep learning models for real-world use cases.

Work closely with data engineering teams to prepare clean, high-quality datasets.

Build scalable ML pipelines using Python, Kubernetes, MLflow, Airflow, etc.

Experiment with advanced techniques such as LLMs, transformers, generative AI, and reinforcement learning.

Optimize models for performance, reliability, and low latency.

Conduct feature engineering, data preprocessing, model tuning, and evaluation.

Integrate AI systems with business applications via APIs or microservices.

Monitor model performance and implement continuous improvement.

Research emerging AI/ML tools and techniques to enhance existing products.

Required Skills & Experience

Strong programming skills in Python (NumPy, Pandas, Scikit-learn).

Hands-on experience with deep learning frameworks: PyTorch, TensorFlow, Keras.

Solid understanding of machine learning algorithms, neural networks, and statistics.

Experience with SQL and NoSQL databases.

Expertise in building and deploying models on AWS, Azure, or GCP.

Familiarity with MLOps tools (MLflow, Docker, Airflow, Git).

Experience working with large datasets and distributed systems.

Strong problem-solving, analytical, and communication skills.

Nice to Have

Experience with LLMs (GPT models), fine-tuning, prompt engineering.

Familiarity with NLP, computer vision, or time-series forecasting.

Knowledge of Databricks, Spark, Hadoop, or other big data tools.

Exposure to CI/CD pipelines, microservice architecture.

Understanding of model governance, bias detection, and ethical AI principles.