AI/ML Engineers - Alpharetta
22 hours ago
Job description
Job Description:
AI/ML Engineer (2026)
Role Summary
We are looking for a highly skilled AI/ML Engineer to design, build, and deploy production-grade AI systems.
You will bridge the gap between data science and operational software, creating intelligent, scalable, and secure applications using cutting-edge models (LLMs, GenAI) and traditional machine learning.
You will focus on turning experimental models into reliable, high-performance, real-world solutions.Key Responsibilities
Productionize ML/AI Models:
Develop, containerize, and deploy machine learning models, including Deep Learning and GenAI, into production environments.
Generative AI & LLMs:
Implement Large Language Models (LLMs) using frameworks like LangChain/LlamaIndex, focusing on prompt engineering, RAG architectures, and fine-tuning.
MLOps Implementation:
Automate CI/CD pipelines, model versioning (DVC), monitoring, and retrain pipelines using MLOps tools (MLflow, Kubeflow).
System Architecture:
Architect scalable, resilient, and secure AI infrastructure on cloud platforms (AWS, Azure, or GCP).
Data Engineering:
Collaborate on ETL pipelines to ensure high-quality data ingestion, preprocessing, and feature engineering for model training.
Model Optimization:
Optimize inference engines (e.g., Triton, vLLM) for low-latency, high-performance model serving.
Responsible AI:
Ensure models are compliant with ethical guidelines, auditing for bias, and implementing Explainable AI (XAI) techniques.
Required Technical Skills & Qualifications:
Education:
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related technical field.
Experience: 3+ years of experience in deploying ML models in production.
Programming:
Expert proficiency in Python (including libraries: NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow).
GenAI Stack:
Experience with Hugging Face, LangChain, vector databases (Pinecone, Milvus), and LLM APIs.
MLOps & Tools:
Hands-on experience with Docker, Kubernetes, MLflow, and CI/CD tools.
Cloud Platforms:
Proficiency with AWS SageMaker, Azure ML, or Vertex AI.
Database Knowledge:
Strong skills in SQL and NoSQL databases.
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