AI/ML Engineer

Position: AI/ML Engineer

Location: Fully remote in the US

Duration: Full-time

Target Salary:

• MIN: $110,628.59
• MID: $138,285.74
• MAX: $165,942.89

MUST HAVES:

• Bachelor's degree in Computer Science, Mathematics, Data Science, or a related discipline, or equivalent experience.
• 4+ years of proven experience as an AI/ML Engineer or similar role, with a strong portfolio of successful projects involving machine learning, NLP, generative AI, and production deployment.
• Experience needs to be HANDS ON
• You built and you coded the solution yourself
• Proficiency in Python, SQL, and AI/ML frameworks and platforms such as:
• PyTorch
• TensorFlow
• Azure OpenAI
• Azure Machine Learning Studio
• Strong understanding of:
• Data structures
• Data pipelines
• MLOps practices
• Working knowledge of generative AI techniques, including:
• Retrieval-Augmented Generation (RAG)
• Embeddings and vector search
• Context orchestration
• Experience with cloud platforms such as Azure and AWS.
• Experience developing and consuming APIs to integrate AI/ML models into applications.

Plusses:

• Master's degree in Data Science or a related field is preferred

Day to Day:

• Project/projects
• Taking the team through the AI journey
• Consultant or self-starter mindset
• Team breakdown
• DNA team - 5 employees
• Consulting team - 40 people
• Data engineers
• BI Engineers
• 1 AI engineer

An Insight Global retail client is seeking an innovative AI Engineer responsible for designing, developing, and deploying AI/ML solutions that solve business problems, predict outcomes, identify inefficiencies, and enable data-driven decision-making. This role leverages advanced modeling, machine learning pipelines, generative AI, and automation to deliver scalable, enterprise-ready AI capabilities.

• Collaborate with cross-functional teams, including data architects, engineers, and business stakeholders, to understand requirements for AI/ML use cases.
• Develop, train, evaluate, and optimize AI/ML models using structured and unstructured data.
• Analyze data and model outputs to identify patterns, trends, and anomalies that inform feature engineering, model improvements, and system optimization.
• Leverage AI/ML platforms to build predictive models and automate business processes.
• Design and implement generative AI applications powered by Large Language Models (LLMs), using techniques such as Retrieval-Augmented Generation (RAG), embeddings, prompt orchestration, and evaluation frameworks.
• Partner with Data Engineering, Analytics, and IT teams to integrate models into production systems.
• Ensure the robustness, scalability, performance, and security of AI/ML models in production environments.
• Monitor, evaluate, and continuously improve AI/ML and LLM-based solutions in production, including model performance, output quality, reliability, and cost efficiency.
• Ensure adherence to AI governance, model documentation, and responsible AI practices.
• Stay up to date with the latest advancements in AI/ML research and industry trends.