What are the responsibilities and job description for the AI Engineer position at Galent?
AI Engineer
Location: Germantown, MD (onsite- Mon-Fri)
We’re seeking enthusiastic AI Engineers to design, build, and deploy intelligent systems spanning generative AI, traditional machine learning, and deep learning. The role offers hands-on opportunities with large language models, AI agents, classical ML pipelines, deep learning architectures, observability tools, responsible AI practices, and cloud infrastructure on GCP.
Responsibilities include:
- Building Python applications powered by LLMs (GPT, Claude, Gemini, LLaMA), utilizing prompt engineering, evaluation, and model customization techniques.
- Developing Retrieval-Augmented Generation (RAG) pipelines with vector databases (FAISS, ChromaDB, Pinecone) and frameworks like LangChain or LlamaIndex.
- Designing and constructing agent workflows that use planning, tool calling, and memory for reliable multi-step tasks, leveraging frameworks such as LangChain, LlamaIndex, CrewAI, or AutoGen.
- Evaluating and improving model outputs using automated metrics and human feedback.
Traditional ML & Deep Learning:
- Training and deploying ML models for classification, regression, and clustering using Python libraries like scikit-learn and XGBoost.
- Performing feature engineering, data preprocessing, and exploratory data analysis on both structured and unstructured datasets.
- Building deep learning models with PyTorch or TensorFlow for NLP and computer vision tasks.
- Applying transfer learning and optimizing models for production (quantization, distillation).
Web Applications & APIs:
- Building web applications and REST APIs with Flask or FastAPI to serve ML, deep learning, and LLM-powered features to end users.
- Designing API endpoints for model inference, data retrieval, and integration with frontend applications.
- Integrating AI capabilities into web services with robust error handling, authentication, and scalable architecture.
Cloud & Deployment:
- Deploying and managing ML, deep learning, and generative AI workloads on Google Cloud Platform (Vertex AI, Cloud Run, GKE, BigQuery).
- Using Vertex AI for training, serving, and orchestrating pipelines across traditional ML models, deep learning models, and LLM-based applications.
- Working with GCP storage (Cloud Storage, BigQuery) for data pipelines and feature stores.
- Containerizing applications with Docker for deployment on GKE or Cloud Run.
- Writing Python scripts for data pipelines, API integrations, and automation tasks on GCP.
- Monitoring deployed ML/DL/LLM models and setting up retraining and evaluation workflows using GCP tools.
Must Have Qualifications:
- Bachelor’s or Master’s in CS, AI, Data Science, or related field.
- Strong proficiency in Python (NumPy, Pandas, Flask/FastAPI) and Hugging Face ecosystem.
- Hands-on experience with LLMs, including prompt engineering, evaluation, or building LLM-powered applications.
- Understanding of ML fundamentals: supervised/unsupervised learning, model evaluation, and feature engineering.
- Deep learning concepts knowledge (Transformers, CNNs, attention mechanisms), with experience in PyTorch or TensorFlow.
- Experience with at least one major cloud platform; GCP strongly preferred.
- Familiarity with Linux, Git, Docker, and building REST APIs using Flask or FastAPI.
- Understanding of databases and SQL for querying and integrating structured data (PostgreSQL, MySQL, BigQuery, MongoDB, or similar).
- Continuous learner with a growth mindset who keeps up with rapidly evolving AI research, tools, and best practices.
- Strong communication skills for explaining complex technical concepts to both technical and non-technical stakeholders.
- Team-oriented mindset with a collaborative approach to problem-solving, code reviews, and knowledge sharing.
- Good documentation habits for writing clear technical docs and maintaining well-commented code.
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, citizenship status, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law. https://www.e-verify.gov/sites/default/files/everify/posters/IER_RighttoWorkPoster.pdf