Advanced Software Engr

Design, develop, train, evaluate, and deploy machine learning and deep learning models.

Develop and optimize Generative AI applications using Large Language Models (GPT, Llama, Claude, Mistral, etc.).

Build Retrieval-Augmented Generation (RAG) solutions using vector databases.

Fine-tune foundation models and implement prompt engineering strategies.

Create scalable AI pipelines for data ingestion, feature engineering, model training, and inference.

Develop AI-powered applications, chatbots, recommendation engines, and predictive analytics solutions.

Implement MLOps practices including automated training, deployment, monitoring, and retraining.

Work with cross-functional teams to translate business requirements into AI solutions.

Optimize model performance, latency, accuracy, and cost efficiency.

Ensure AI governance, model explainability, privacy, and responsible AI practices.

Stay current with emerging AI technologies and recommend innovations.

Machine Learning & AI

Generative AI

Strong understanding of: Supervised and Unsupervised Learning, Regression and Classification, Clustering, Recommendation Systems, Time-Series Forecasting, Reinforcement Learning, Deep Learning

Experience with: LLMs (GPT, Llama, Gemini, Claude, Mistral), RAG architectures, Prompt Engineering, Fine-tuning and PEFT techniques,  LangChain / LangGraph, Semantic Kernel, AI Agents and Multi-Agent Systems

Programming

Expert-level Python development

Strong knowledge of: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch

MLOps & Deployment: Docker, Kubernetes, MLflow, CI/CD pipelines, Model Monitoring, Azure ML / AWS SageMaker / Vertex AI

Data Technologies: SQL, Spark/PySpark, Data Engineering concepts, ETL pipelines, Feature Stores

Vector Databases: Pinecone, ChromaDB, Weaviate, Azure AI Search, FAISS

Cloud Platforms

Experience in at least one: