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AI/ML & Generative AI Engineer

Cognitive Minds LLCCA🇺🇸United StatesPosted 31 Jul 2026

Why This Role Stands Out

This remote role offers a fantastic opportunity to build cutting-edge generative AI applications and shape impactful AI/ML solutions, with excellent potential for skill development in a collaborative environment. You'll thrive here if you're a seasoned AI/ML Engineer eager to leverage your expertise in Python, generative AI, and cloud platforms to drive innovation. Apply today to contribute to pioneering projects and expand your career horizons.

Quick Overview

Work Type
Remote
Level
Mid Senior

Job Description

Job Title: AI/ML Engineer
Location: Remote
Employment Type: Contract (W2)
Job Summary
We are seeking an experienced AI/ML Engineer/Developer to design, develop, deploy, and maintain scalable artificial intelligence and machine learning solutions. The ideal candidate will have strong experience with Python, machine learning frameworks, data processing, model development, generative AI, and cloud platforms. This role requires close collaboration with data scientists, software engineers, product teams, and business stakeholders to transform business requirements into production-ready AI/ML applications.
Key Responsibilities
  • Design, develop, train, evaluate, and optimize machine learning and deep learning models.
  • Build end-to-end AI/ML pipelines for data preparation, feature engineering, training, validation, deployment, and monitoring.
  • Develop generative AI applications using large language models, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation.
  • Integrate AI/ML models with enterprise applications through REST APIs and microservices.
  • Process and analyze structured and unstructured data from multiple sources.
  • Deploy scalable models and services on cloud platforms such as AWS, Azure, or Google Cloud.
  • Implement MLOps practices, including model versioning, automated deployment, performance monitoring, and retraining.
  • Evaluate models using appropriate performance, accuracy, reliability, fairness, and explainability metrics.
  • Troubleshoot production issues and improve model performance, latency, scalability, and security.
  • Collaborate with cross-functional teams to define requirements and deliver business-focused AI solutions.
  • Maintain technical documentation and follow software development, data governance, and responsible AI best practices.
Required Qualifications
  • Bachelor's or master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field.
  • Professional experience developing and deploying machine learning or AI solutions in production environments.
  • Strong programming skills in Python and experience with libraries such as Pandas, NumPy, and Scikit-learn.
  • Hands-on experience with TensorFlow, PyTorch, or similar machine learning frameworks.
  • Strong understanding of supervised and unsupervised learning, deep learning, NLP, and model evaluation techniques.
  • Experience with data preprocessing, feature engineering, model optimization, and hyperparameter tuning.
  • Experience developing APIs and production applications using frameworks such as FastAPI or Flask.
  • Knowledge of SQL and experience working with relational and NoSQL databases.
  • Experience with Git, Docker, CI/CD pipelines, and cloud-based development.
  • Strong analytical, problem-solving, communication, and collaboration skills.
Preferred Qualifications
  • Experience with generative AI, LLMs, prompt engineering, fine-tuning, RAG, AI agents, and guardrails.
  • Knowledge of LangChain, LlamaIndex, Hugging Face, OpenAI-compatible APIs, or similar technologies.
  • Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, or Chroma.
  • Familiarity with Kubernetes and MLOps tools such as MLflow, Kubeflow, SageMaker, Azure Machine Learning, or Vertex AI.
  • Understanding of responsible AI, model explainability, data privacy, bias detection, and security.
  • Experience with distributed data-processing platforms such as Spark or Databricks.
  • Familiarity with Agile/Scrum development environments.

Skills

Docker
FastAPI
Flask
Microservices
SQL
AWS
MLOps
MLflow
Machine Learning
NLP
NumPy
Scikit-learn
Scrum
Agile
Azure
Databricks
Deep Learning
Generative AI
Git
Google Cloud
Hugging Face
Kubernetes
Pandas
PyTorch
Python
REST
TensorFlow

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