AI/ML Engineer

MoonWise Career; United States; On-site; USD 120,000 - 160,000; Full time

Job summary

MoonWise Career is seeking an AI/ML Engineer to design, develop, deploy, and optimize machine learning solutions that solve real-world business problems. You will work with data scientists, software engineers, product teams, and business stakeholders to build scalable AI/ML systems from experimentation through production.

Key focus areas include end-to-end ML pipelines, deep learning models with PyTorch/TensorFlow, Generative AI and LLM-based apps, and production services via FastAPI and cloud

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics

Responsibilities

• Design, develop, train, evaluate, and deploy machine learning models for business and product use cases.
• Develop end-to-end ML pipelines, including data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
• Apply supervised and unsupervised learning techniques to structured and unstructured data.
• Build and optimize deep learning models using frameworks such as PyTorch and TensorFlow.
• Develop Generative AI and LLM-based applications, including RAG, prompt engineering, embeddings, vector search, and AI agents.
• Integrate foundation models and LLM APIs into production applications.
• Develop scalable inference services and ML APIs using FastAPI, REST APIs, and Python.
• Implement MLOps practices for model versioning, CI/CD, experiment tracking, deployment, monitoring, and model lifecycle management.
• Deploy machine learning solutions using cloud platforms such as AWS, Azure, or Google Cloud.
• Work with databases, data warehouses, and distributed data-processing technologies to prepare high-quality training datasets.
• Monitor model performance, data drift, model drift, latency, reliability, and production accuracy.
• Optimize models for performance, scalability, cost, and inference latency.
• Collaborate with cross-functional teams to translate business requirements into practical AI/ML solutions.
• Conduct experiments, analyze results, and continuously improve model performance.
• Maintain technical documentation for models, pipelines, APIs, and production systems.

Skills

Python

SQL

Java

C++

NLP

Transformers

MLOps

Cloud platforms

Git

Leadership

Education

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics

Tools

PyTorch

TensorFlow

scikit-learn

LangChain

LlamaIndex

Hugging Face

Pinecone

FAISS

Weaviate

Milvus

OpenSearch

MLflow

Kubeflow

SageMaker

Vertex AI

Azure ML

Job description

We are looking for a skilled AI/ML Engineer to design, develop, deploy, and optimize machine learning solutions that solve real-world business problems. The ideal candidate will have hands‑on experience with Python, machine learning, deep learning, generative AI, LLMs, MLOps, and cloud platforms.

You will work closely with data scientists, software engineers, product teams, and business stakeholders to build scalable AI/ML systems from experimentation through production.

Key Responsibilities

• Design, develop, train, evaluate, and deploy machine learning models for business and product use cases.
• Develop end-to-end ML pipelines, including data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
• Apply supervised and unsupervised learning techniques to structured and unstructured data.
• Build and optimize deep learning models using frameworks such as PyTorch and TensorFlow.
• Develop Generative AI and LLM-based applications, including RAG, prompt engineering, embeddings, vector search, and AI agents.
• Integrate foundation models and LLM APIs into production applications.
• Develop scalable inference services and ML APIs using FastAPI, REST APIs, and Python.
• Implement MLOps practices for model versioning, CI/CD, experiment tracking, deployment, monitoring, and model lifecycle management.
• Deploy machine learning solutions using cloud platforms such as AWS, Azure, or Google Cloud.
• Work with databases, data warehouses, and distributed data-processing technologies to prepare high‑quality training datasets.
• Monitor model performance, data drift, model drift, latency, reliability, and production accuracy.
• Optimize models for performance, scalability, cost, and inference latency.
• Collaborate with cross‑functional teams to translate business requirements into practical AI/ML solutions.
• Conduct experiments, analyze results, and continuously improve model performance.
• Maintain technical documentation for models, pipelines, APIs, and production systems.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, or a related field.
• 3+ years of professional experience in Machine Learning, Artificial Intelligence, Data Science, or Software Engineering.
• Strong programming skills in Python.
• Strong understanding of machine learning algorithms, statistics, probability, and model evaluation.
• Hands‑on experience with scikit‑learn, PyTorch, TensorFlow, or similar ML frameworks.
• Experience building and deploying machine learning models in production environments.
• Experience with SQL and data‑processing workflows.
• Strong understanding of software engineering principles, Git, testing, debugging, and API development.
• Experience with at least one major cloud platform: AWS, Azure, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and production deployment practices.
• Strong problem‑solving and analytical skills.

Preferred Qualifications

• Experience with Generative AI, Large Language Models (LLMs), RAG, embeddings, vector databases, and AI agents.
• Experience with frameworks such as LangChain, LlamaIndex, Hugging Face, or equivalent tools.
• Experience with vector databases such as Pinecone, FAISS, Weaviate, Milvus, or OpenSearch.
• Experience with MLOps platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
• Experience with distributed computing technologies such as Spark.
• Experience deploying ML workloads on AWS SageMaker, Azure ML, or Google Vertex AI.
• Knowledge of model optimization, quantization, fine‑tuning, and inference optimization.
• Experience with NLP, computer vision, recommendation systems, forecasting, or other applied ML domains.
• Familiarity with responsible AI, model security, data privacy, and AI governance.

Core Technical Skills

Programming: Python, SQL, Java, C++

Generative AI: LLMs, RAG, Prompt Engineering, Embeddings, Fine‑Tuning, AI Agents

NLP: Transformers, Hugging Face, Text Classification, Semantic Search

APIs: FastAPI, REST APIs, Microservices

What Success Looks Like

• Production‑ready ML models that deliver measurable business impact.
• Reliable and scalable AI/ML pipelines.
• Improved model accuracy, latency, and operational efficiency.
• Well‑monitored and maintainable ML systems.
• Successful integration of Generative AI and LLM capabilities into products and business workflows.
• Strong collaboration between engineering, data science, product, and business teams.