Description

- 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

Key Skills

Education

Bachelor's or Master's degrees