Machine Learning Engineer; W2

We are seeking an experienced Machine Learning Engineer to build, deploy, and scale machine learning solutions in cloud-native production environments. The ideal candidate will combine strong software engineering skills with expertise in MLOps, cloud technologies, and production-grade AI systems.

Key Responsibilities:

• Design, develop, and deploy machine learning models and AI applications into production environments.
• Build scalable training, inference, and feature engineering pipelines.
• Develop MLOps frameworks for model versioning, monitoring, retraining, and governance.
• Collaborate with Data Scientists, Data Engineers, and Product teams to deliver end-to-end machine learning solutions.
• Build APIs and microservices to expose machine learning models for enterprise applications.
• Implement CI/CD pipelines for automated testing and deployment of ML solutions.
• Monitor production systems for model drift, performance degradation, and operational issues.
• Optimize models for latency, scalability, and cost efficiency.
• Create technical documentation and architectural design artifacts.

Required Skills:

• Strong programming skills in Python, SQL, and software engineering principles.
• Experience with Tensor Flow, PyTorch, Scikit-learn, and XGBoost.
• Hands-on experience with Docker, Kubernetes, and container orchestration.
• Experience with AWS services such as Sage Maker, Lambda, ECS, EKS, S3, and Redshift.
• Experience with Azure Machine Learning or Databricks is a plus.
• Strong understanding of CI/CD tools including Jenkins, Git Hub Actions, and Azure Dev Ops.
• Experience building REST APIs using FastAPI or Flask.
• Familiarity with Spark, PySpark, and distributed computing frameworks.
• Experience with MLflow, Kubeflow, Airflow, and model monitoring tools.

Preferred Qualifications:

• Experience with Large Language Models (LLMs), RAG architectures, prompt engineering, and AI agents.
• Experience with Lang Chain, Hugging Face, OpenAI APIs, and Vector Databases such as Pinecone, FAISS, or ChromaDB.
• Experience in highly regulated industries such as Banking, Healthcare, and Insurance.
• Strong understanding of system design, scalability, and cloud architecture.
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field.