AI/ML Engineer

Position Overview

We are seeking a hands-on AI/ML Engineer to design, develop, deploy, and optimize machine learning and generative AI solutions that solve real business problems and create measurable impact, using Python, modern ML frameworks, LLMs, and cloud platforms.

Key Responsibilities

• Build, train, evaluate, and deploy machine learning models across NLP, LLM, computer vision, or predictive analytics use cases.
• Develop robust data pipelines and feature engineering workflows that turn raw data into reliable, model-ready datasets.
• Fine-tune foundation models and implement Retrieval-Augmented Generation (RAG) architectures for generative AI applications.
• Run experiments and benchmark models to select the right approach, then monitor model performance in production and apply MLOps best practices.
• Collaborate with product, engineering, and business teams to translate business problems into AI solutions and keep delivery aligned with project goals.

Technical Skills

Mandatory

• Python (NumPy, Pandas, Scikit-learn); PyTorch and/or TensorFlow
• Machine Learning and Deep Learning fundamentals
• LLMs, Prompt Engineering, Vector Databases, and RAG
• SQL and data processing
• Azure, AWS, or GCP; Git, CI/CD, and MLOps concepts

Good to Have

• Azure AI, OpenAI, LangChain, Semantic Kernel, or similar frameworks
• Docker and Kubernetes
• Model deployment and monitoring in production environments

Required Qualifications

• Bachelors or masters degree in Computer Science, AI, Data Science, Statistics, or related field with 2 to 5 years of hands-on experience in machine learning or AI development.
• Hands-on experience taking ML or generative AI models from experimentation through to deployment.
• Solid understanding of model evaluation, experimentation, and benchmarking practices.

Preferred Skills

• Strong analytical and problem-solving skills, with a structured, experiment-driven approach.
• Excellent communication skills, with the ability to explain model results and trade-offs to technical and non-technical stakeholders.
• Commitment to high-quality model performance, scalable deployments, and continuous improvement of AI solutions.