AI Engineer

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.

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.