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Must-Have
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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.
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Good-to-Have
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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.
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