AI Engineer

No. of positions - 2


Location - Onsite at Germantown, MD


Visa Independents only


All 5 days onsite


Rate: Market

AI Engineer (Contract, 0-2 Years Experience)

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.


- 0-3 years of experience (internships, research, or personal projects count).


- 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.

If you have any questions or need further clarification, feel free to reach out.