AI/ML Engineer

Key Responsibilities

• Design, develop, and deploy machine learning models for business applications.
• Collect, clean, preprocess, and analyze structured and unstructured data.
• Build and optimize supervised and unsupervised machine learning models.
• Perform feature engineering and model selection.
• Train, evaluate, and fine-tune ML models using appropriate performance metrics.
• Develop data pipelines for model training and inference.
• Deploy machine learning models into production environments.
• Monitor model performance and improve models based on new data.
• Work with data scientists, software engineers, and business teams.
• Conduct experiments and document model performance and results.
• Implement scalable and maintainable AI/ML solutions.
• Stay updated with emerging AI/ML technologies and research.

Required Technical Skills

• Python
• Strong understanding of Machine Learning concepts and algorithms
• NumPy, Pandas, Scikit-learn
• Data preprocessing and feature engineering
• Model evaluation and validation
• SQL and database fundamentals
• Git/GitHub
• Basic understanding of statistics and probability
• Experience with ML frameworks such as TensorFlow or PyTorch

Deep Learning Skills

• Neural networks and deep learning fundamentals
• CNNs and RNNs
• Transfer learning
• Model optimization and fine-tuning
• Familiarity with GPU-based model training

Generative AI / LLM Skills

• Understanding of Generative AI and Large Language Models (LLMs)
• Prompt engineering
• Embeddings and vector databases
• Retrieval-Augmented Generation (RAG)
• Familiarity with transformer architectures
• Experience with LLM APIs and/or open-source models
• Basic understanding of AI agents and tool calling

MLOps & Deployment Skills

• Model deployment using FastAPI, Flask, or similar frameworks
• Docker and containerization
• CI/CD fundamentals
• Cloud platforms such as AWS, Azure, or Google Cloud
• Model monitoring and versioning
• Familiarity with MLflow or similar MLOps tools
• Understanding of REST APIs and microservices

Preferred Skills

• NLP and computer vision
• Time-series forecasting
• Hugging Face Transformers
• LangChain or similar AI frameworks
• Vector databases such as FAISS, Pinecone, or similar technologies
• Kubernetes
• Spark or other distributed data-processing technologies