AI / ML Engineer

Job Summary

We are seeking a skilled AI / ML Engineer to design, build, and deploy production-ready machine learning models and AI applications. In this role, you will bridge the gap between data science and software engineering, taking models from experimental stages to scalable production environments. You will work closely with data scientists, data engineers, and product teams to integrate AI capabilities into our core platform.

Core Responsibilities

• Model Development & Tuning: Design, train, and optimize machine learning, deep learning, and Natural Language Processing (NLP) models.
• Production Deployment: Build, scale, and maintain robust MLOps pipelines to deploy models in cloud environments.
• Data Engineering: Architect and optimize data pipelines, feature stores, and preprocessing workflows to handle large-scale datasets.
• LLM Integration: Evaluate, fine-tune, and integrate Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks where applicable.
• Performance Optimization: Monitor, benchmark, and improve the latency, throughput, and cost-efficiency of inference systems.
• Collaboration: Partner with cross-functional teams to translate business requirements into technical AI solutions.

Required Technical Skills & Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related quantitative field.
• Programming: Advanced proficiency in Python and standard data science libraries (NumPy, Pandas, Scikit-Learn).
• Deep Learning Frameworks: Hands-on experience with PyTorch or TensorFlow/Keras.
• Cloud & Infrastructure: Experience deploying models on AWS, Google Cloud Platform, or Azure using containerization tools like Docker and Kubernetes.
• MLOps Tools: Familiarity with ML tracking and deployment tools such as MLflow, Kubeflow, or SageMaker.
• Databases: Experience with SQL/NoSQL databases and vector databases (e.g., Pinecone, Milvus, Chroma).

Preferred Qualifications

• Experience with generative AI frameworks like LangChain or LlamaIndex.
• Familiarity with distributed computing frameworks like Apache Spark or Ray.
• Solid understanding of software engineering best practices (CI/CD, Git, unit testing).