AI/ML & Generative AI Engineer – Entry Level (1–6 Years)
Location: United States ( Remote)
About the Role
We are looking for a motivated and innovative AI/ML & Generative AI Engineer to join our growing technology team. This opportunity is ideal for professionals with 1–6 years of experience who are passionate about Artificial Intelligence, Machine Learning, Generative AI, and building real-world AI-powered solutions.
You will work on developing, testing, and deploying machine learning and Generative AI applications while collaborating with software engineers, data scientists, product teams, and business stakeholders.
Candidates with experience in Python, Machine Learning, Generative AI, LLMs, NLP, Deep Learning, or AI application development are encouraged to apply.
Key Responsibilities
Develop and implement machine learning and AI solutions for real-world business problems.
Build and integrate Generative AI and Large Language Model (LLM) applications.
Develop AI-powered applications using Python and modern AI/ML frameworks.
Work with LLM APIs, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG).
Develop and evaluate machine learning models for classification, prediction, recommendation, NLP, and other use cases.
Prepare, clean, transform, and analyze datasets for model development.
Experiment with different AI/ML algorithms and evaluate model performance.
Fine-tune or adapt AI/ML models when appropriate for business requirements.
Build APIs and services to integrate AI/ML models with applications.
Collaborate with software engineers to deploy AI solutions into production environments.
Monitor model performance, accuracy, reliability, and data quality.
Troubleshoot and optimize AI/ML applications for performance and scalability.
Stay current with emerging technologies in Generative AI, LLMs, Machine Learning, and Deep Learning.
Document technical solutions, experiments, models, and implementation processes.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related technical field.
1–6 years of professional experience in AI/ML, Machine Learning, Data Science, Software Engineering, or a related field.
Strong programming skills in Python.
Understanding of fundamental Machine Learning and Artificial Intelligence concepts.
Experience with at least one ML framework such as TensorFlow, PyTorch, Scikit-learn, or Keras.
Understanding of data structures, algorithms, statistics, and model evaluation.
Familiarity with Generative AI and Large Language Models (LLMs).
Strong SQL and data manipulation skills.
Good analytical, problem-solving, and communication skills.
Generative AI / LLM Skills
Experience with some of the following is highly desirable:
OpenAI or other LLM APIs
Generative AI applications
Prompt Engineering
Retrieval-Augmented Generation (RAG)
Embeddings
Vector Databases
LangChain, LlamaIndex, or similar frameworks
NLP and text-processing applications
Model evaluation and experimentation
AI agents and workflow automation
Fine-tuning or adapting foundation models
Preferred Qualifications
Experience with AWS, Microsoft Azure, or Google Cloud Platform (GCP).
Experience deploying ML/AI applications using REST APIs or microservices.
Familiarity with Docker and Kubernetes.
Experience with Git and CI/CD practices.
Knowledge of ML pipelines and MLOps.
Experience with databases such as PostgreSQL, MySQL, MongoDB, or similar technologies.
Experience with Databricks, Snowflake, or other modern data platforms.
Familiarity with cloud-based AI/ML services.
Experience working in Agile/Scrum environments.
Strong interest in emerging AI technologies and the ability to quickly learn new tools.
What You'll Work On
Depending on your experience and interests, you may work on:
Generative AI applications
LLM-powered business solutions
AI chatbots and virtual assistants
RAG-based knowledge systems
Predictive machine learning models
Natural Language Processing (NLP)
Recommendation systems
AI-powered automation
Document intelligence
Data-driven applications
Model deployment and monitoring
AI APIs and production integrations
