AI ML Engineer

Desired Competencies (Technical/Behavioral Competency ) Must-Have Strong hands-on experience in Python for AI/ML development. Experience in building, training, validating, and deploying Machine Learning models using libraries such as NumPy, Pandas, Scikit-learn , or similar. Hands-on experience in data preprocessing, feature engineering, model selection, model evaluation, and performance tuning . Good understanding of ML algorithms such as classification, regression, clustering, recommendation systems, forecasting , or similar. Experience in NLP techniques such as text classification, entity extraction, sentiment analysis, embeddings, semantic search, or document processing. Exposure to Generative AI / LLM-based solutions , prompt engineering, embeddings, semantic search, RAG-based solutions, or AI application development. Good knowledge of SQL and ability to work with structured and unstructured data from multiple sources. Experience with model deployment, API integration, monitoring, and production support . Good understanding of MLOps concepts , including model versioning, experiment tracking, CI/CD for ML, monitoring, retraining, and model lifecycle management. Strong analytical, problem-solving, and communication skills with ability to work with business, data, engineering, and product teams. Good-to-Have Experience in Generative AI frameworks and tools , including LangChain, LangGraph, AutoGen , or similar frameworks. Hands-on experience in Large Language Models, transformer-based systems, LLM-integrated pipelines , and GenAI application development. Exposure to GANs, diffusion models , and advanced generative AI techniques. Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras . Experience building APIs using Flask, FastAPI , or similar Python-based API frameworks. Experience in AI/ML use cases across Generative AI, NLP, Computer Vision, Industrial Analytics, automation, or human-computer interaction . Experience building scalable ML pipelines covering data ingestion, preprocessing, model training, evaluation, deployment, monitoring, and retraining . Exposure to cloud platforms such as Azure, AWS, or GCP and related AI/ML services. Exposure to Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI APIs , or similar AI platforms. Experience with vector databases such as FAISS, Pinecone, Chroma, Weaviate, Azure AI Search , or similar. Experience in developing RAG pipelines, chatbots, document intelligence solutions, knowledge assistants, or enterprise AI applications . Exposure to Docker, Kubernetes, MLflow, Airflow, Kubeflow, Databricks , or similar tools. Understanding of model robustness, explainability, fairness, privacy, security, and data governance standards . Must be well versed or have exposure to Agile methodology and software engineering best practices . </table