AI/ML Intern

Smith & Associates•Houston, TX

22h•Onsite

About The Position

Qualified candidate will work Monday through Friday, 40 hours a week. As an AI/ML Intern, you will be responsible for the end-to-end development and deployment of production-grade machine learning solutions, with a specialized focus on large language models (LLMs) and agentic workflows. You will design and implement sophisticated RAG pipelines, develop multi-agent systems involving complex planning and tool use, and build diverse models across NLP, computer vision, and predictive analytics. Your role involves writing high-quality Python code to create robust data pipelines, integrating SQL/NoSQL databases and vector stores, and deploying services on AWS infrastructure using S3, Lambda, and SageMaker. Additionally, you will be expected to establish rigorous evaluation frameworks and guardrails, monitor production performance—specifically regarding latency and model drift—and maintain technical excellence by rapidly prototyping emerging research into functional, scalable features.

Requirements

Pursuing a bachelor's degree in computer science, computer engineering, or software engineering, with a core background in AI/ML
Strong understanding of algorithms and data structures, with the ability to analyze complexity and select appropriate approaches
Solid grasp of core machine learning principles, including bias and variance, feature engineering, cross-validation, regularization, and metric evaluation
Familiarity with LLM fundamentals, such as tokenization, model families, fine-tuning, RAG patterns, and LLM evaluations
Exposure to agentic AI concepts, including tool calling, planning, memory management and basic orchestration of multi-agent systems
Knowledge of Model Context Protocol (MCP) for secure context sharing, integrations, and tool orchestration
Proficiency in Python and common libraries (NumPy, pandas, scikit-learn, with PyTorch or TensorFlow preferred
Comfortable with AWS basics (IAM, S3, compute/container runtimes) or comparable cloud platforms
Experience with writing efficient SQL, understanding database normalization and indices, and basic familiarity with NoSQL databases like DynamoDB
Awareness of vector databases such as FAISS, Pinecone, or Milvus is a plus
Competence with version control using Git and adherence to software craftsmanship practices like testing, linting, and code reviews
Qualified candidates must be a self-starter; results oriented, and have a previous track record of achievement
Candidate must be pursuing a Bachelor’s Degree in Computer Science
Students with F-1 visas should consult with their designated school official (DSO) before applying.

Nice To Haves

Understanding of data engineering fundamentals, including Airflow or Prefect, message queues, and data validation techniques
Experience in API development using FastAPI or Flask, and exposure to observability practices such as logging, metrics, and tracing
Awareness of security, privacy, and responsible AI concerns, including handling personally identifiable information (PII), prompt injection basics, and a red-teaming mindset
Comfort with mathematical topics like linear algebra, probability and calculus

Responsibilities

End-to-end development and deployment of production-grade machine learning solutions
Design and implement sophisticated RAG pipelines
Develop multi-agent systems involving complex planning and tool use
Build diverse models across NLP, computer vision, and predictive analytics
Write high-quality Python code to create robust data pipelines
Integrate SQL/NoSQL databases and vector stores
Deploy services on AWS infrastructure using S3, Lambda, and SageMaker
Establish rigorous evaluation frameworks and guardrails
Monitor production performance—specifically regarding latency and model drift
Maintain technical excellence by rapidly prototyping emerging research into functional, scalable features