Machine Learning Engineer - AI Applications

• This role is responsible for specializing in development and deployment of machine learning applications that drive innovation and business value. The role researches and implements appropriate ML algorithms and tools, runs machine learning tests and experiments, and trains and retrains systems when necessary. The role contributes to deploying models in production environments and ensuring their seamless integration.

Responsibilities

• Develops and programs integrated software solutions, especially in support of the development, deployment and life cycle of machine learning and generative AI models.

• Trains and validates models using appropriate evaluation metrics and techniques, including prompt engineering and fine-tuning of LLMs.

• Optimizes and tunes hyperparameters to improve model performance and generalization across both traditional ML and GenAI systems.

• Deploys ML and GenAI models to production environments, ensuring scalability, reliability, and efficiency.

• Monitors and maintains deployed models, making necessary updates to adapt to changing data or requirements.

• Spearheads the design of ML and GenAI systems, experiments with algorithms, and regularly trains systems for maximum efficiency.

• Designs, develops, tests, and maintains ML and GenAI pipelines that efficiently produce scalable models and services.

• Cooperates with CI/CD teams to ensure seamless deployment of models to production, including containerization and orchestration of GenAI services.

• Partners with research and engineering teams to enhance automated model training techniques and GenAI capabilities such as retrieval-augmented generation (RAG), embeddings, and vector databases.

• Applies machine learning, generative AI, and statistical modeling techniques to business or research problems, including text generation, summarization, image synthesis, and conversational AI.

Education & Experience Recommended

• Bachelor’s or Graduate Degree in Computer Science, Statistics, Mathematics, Data Science, Artificial Intelligence, or a related discipline — or equivalent professional experience demonstrating strong competence in machine learning and generative AI.

• Typically possesses 4–7 years of experience in software development, machine learning, generative AI, statistical modeling, or related fields. Candidates with an advanced degree may have 3–5 years of relevant experience, including hands-on work with large language models (LLMs), multimodal GenAI systems, and model deployment in production environments.

Knowledge & Skills

• Agile Methodology

• Algorithms

• Azure Cloud

• Artificial Intelligence

• Automation

• Big Data

• Computer Science

• Data Science

• Deep Learning

• Generative AI (GenAI)

• Large Language Models (LLMs)

• Prompt Engineering

• Retrieval-Augmented Generation (RAG)

• LangChain

• Hugging Face Transformers

• OpenAI API

• Vector Databases (e.g., FAISS, AI Search, ElasticSearch)