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5 days ago
This is a key role in our organization where you will be working on the development and integration of large language models (LLMs) into enterprise applications.
- Key Responsibilities:
- Develop, fine-tune, and integrate LLMs (GPT, LLaMA, Claude, etc.) into enterprise applications to create seamless user experiences.
- Implement prompt engineering techniques, embeddings, and retrieval augmented generation pipelines to enhance model performance.
- Collaborate with cross-functional teams to build and maintain APIs, microservices, and front-end/back-end integrations for GenAI applications.
- Work with vector databases (Pinecone, FAISS, Weaviate, Milvus) to enable semantic search and improve data retrieval.
- Deploy solutions on cloud AI platforms (Azure OpenAI, AWS Bedrock, GCP Vertex AI) to ensure scalability and reliability.
- Optimize model performance, latency, and scalability for real-world usage by analyzing data and implementing strategies.
- Ensure AI solutions follow responsible AI, data privacy, and security guidelines to maintain trust and transparency.
- Technical Skills:
- Strong programming skills in Python (preferred), with experience in APIs, microservices, Flask/FastAPI/Django to develop scalable and efficient software.
- Hands-on experience with Azure Databricks and Apache Spark to work with big data and analytics.
- Knowledge of vector embeddings and vector databases to improve data retrieval and analysis.
- Experience with LangChain, LlamaIndex, or similar orchestration frameworks to manage complex workflows.
- Familiarity with Docker, Kubernetes, and MLOps practices for deploying AI models to production environments.
- Strong problem-solving and debugging skills to troubleshoot complex technical issues.
- Good to have experience with PyTorch, TensorFlow, Hugging Face Transformers to leverage state-of-the-art AI technologies.
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