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• Develop and implement AI/ML solutions using Python and key ML libraries such as PyTorch, scikit-learn, and Hugging Face
• Design, build, and deploy GenAI and LLM-based applications including RAG systems, chat assistants, copilots, content generation, summarization, and workflow automation
• Work with agent frameworks (LangGraph, Semantic Kernel, LangChain, AutoGen, CrewAI, OpenAI Assistants) and integrate external tools
• Deploy and manage solutions on cloud GenAI stacks like Azure OpenAI / Azure AI Studio, AWS Bedrock, or Google Vertex AI, including vector search implementation
• Implement evaluation and safety guardrails, including jailbreak/prompt-injection mitigation, PII redaction, and compliance-focused design
• Build enterprise copilots or assistants integrated with business systems such as ticketing, CRM/ERP, data lakes, and search
• Apply routing, mixture-of-models, and cost-aware policy design for efficient LLM usage
• Design dashboards and perform operational/product analytics using tools like Power BI or Superset
Job ID: 144986795
Skills:
snowflake , MLops, Docker, Terraform, Data Integration, Azure, AWS, generative AI, data orchestration
Skills:
Java, Node.js, Gcp, Azure, Python, AWS, LangChain, LLMs, Hugging Face Transformers, vector databases, MLflow, embedding models, Vertex AI, Kubeflow, Transformers, RAG, Diffusion models
Skills:
Sql, Gcp, Neo4j, Azure, Python, AWS, LangChain, LLMs, Cypher, LangGraph, graph query languages, RAG agents, Amazon Neptune, Knowledge Graphs
Skills:
data engineering , S3, Kafka, Emr, Data Modeling, Redshift, Sql, Apache Airflow, Kinesis, Spark, Python, AWS Cloud services, DataOps practices, AWS Step Functions, CI CD, SageMaker, pipeline orchestration, Lake Formation, Glue
Skills:
containerization , Python, Apis, Machine Learning, MLops, LangChain, LLMs, RAG architectures, LLMOps, Generative AI, LangGraph, vector databases
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