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AI ML Engineer
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- Posted 3 hours ago
- Be among the first 10 applicants
Job Description
Strong programming skills in Python. Experience with Machine Learning libraries such as Scikit-learn, XGBoost, TensorFlow, Keras, or PyTorch. Good understanding of Supervised & Unsupervised Learning, Deep Learning, NLP, and Computer Vision. Experience with data analysis tools like Pandas, NumPy, Matplotlib. Knowledge of SQL and working with large datasets. Experience with REST APIs and model deployment. Familiarity with Git version control
Design, develop, train, and optimize Machine Learning and Deep Learning models. Analyze structured and unstructured datasets to identify business insights and opportunities. Build and maintain data pipelines for model training and inference. Develop AI solutions using NLP, Computer Vision, Recommendation Systems, or Predictive Analytics. Deploy ML models using cloud platforms and MLOps best practices. Perform feature engineering, model evaluation, hyperparameter tuning, and performance optimization. Collaborate with Data Scientists, Software Engineers, and business stakeholders. Monitor model performance and retrain models as required. Document model architecture, experiments, and deployment processes
Exposure to Generative AI, LLMs, LangChain, RAG, Vector Databases (Pinecone, ChromaDB, FAISS). Experience with cloud platforms such as AWS, Azure, or GCP. Knowledge of Docker, Kubernetes, CI/CD, and MLOps tools. Familiarity with MLflow, Airflow, or Kubeflow. Experience with prompt engineering and AI agent frameworks
Design, develop, train, and optimize Machine Learning and Deep Learning models. Analyze structured and unstructured datasets to identify business insights and opportunities. Build and maintain data pipelines for model training and inference. Develop AI solutions using NLP, Computer Vision, Recommendation Systems, or Predictive Analytics. Deploy ML models using cloud platforms and MLOps best practices. Perform feature engineering, model evaluation, hyperparameter tuning, and performance optimization. Collaborate with Data Scientists, Software Engineers, and business stakeholders. Monitor model performance and retrain models as required. Document model architecture, experiments, and deployment processes
Exposure to Generative AI, LLMs, LangChain, RAG, Vector Databases (Pinecone, ChromaDB, FAISS). Experience with cloud platforms such as AWS, Azure, or GCP. Knowledge of Docker, Kubernetes, CI/CD, and MLOps tools. Familiarity with MLflow, Airflow, or Kubeflow. Experience with prompt engineering and AI agent frameworks
More Info
Key Skills
MLflow
Pinecone
Vector Databases
Kubeflow
ChromaDB
LangChain
Generative AI
LLMs
Scikit-learn
FAISS
RAG
CI/CD
About Company
Skills:
Machine Learning, Technology, AI-Data science, AI-Generative AI, ethical ai, Responsible AI, Prompt Engineering, AI-Machine Learning
Skills:
data engineering , Technical Leadership, python, Pytorch, Deep Learning, Machine Learning, Numpy, MLops, Azure, retrieval-augmented generation, vector search, langchain, mentorship, model deployment, embeddings, langgraph, llmops, agentic ai, production integration, model serving, prompt engineering, pipeline architecture
Skills:
Ml, Cloud Migration, Software Engineering, multi-agent systems, Generative AI, application transformation, LLMs, Ai, AI-assisted application modernization, Security, Agentic AI, platform upgrades, responsible AI practices, Governance, observability, RAG architectures, autonomous agents
Skills:
Python, vector stores, data pipeline development, retrieval strategies, re-ranking, multi-step agent workflows, embedding models, prompt engineering, RAG architectures
Skills:
Image Processing, Data Science, deep learning frameworks, chatbot development, production-quality AI pipelines, generative AI models, AI applications, neural network architectures, cloud AI services, on-premises infrastructure