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Key Responsibilities
- Develop, fine-tune, and integrate LLMs (GPT, LLaMA, Claude, etc.) into enterprise applications.
- Implement prompt engineering techniques, embeddings, and RAG (Retrieval Augmented Generation) pipelines.
- Understanding of building Agentic AI applications using Langgraph, etc.
- 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.
- Deploy solutions on cloud AI platforms (Azure OpenAI, AWS Bedrock, GCP Vertex AI).
- Optimize model performance, latency, and scalability for real-world usage.
- Collaborate with data scientists and solution architects to deliver PoCs and production-ready applications.
- Ensure AI solutions follow responsible AI, data privacy, and security guidelines.
Required Skills
- Strong programming skills in Python (preferred), with experience in APIs, microservices, Flask/FastAPI/Django.
- Hands-on experience with Azure Databricks and Apache Spark.
- Knowledge of vector embeddings and vector databases.
- Experience with LangChain, LlamaIndex, or similar orchestration frameworks.
- Familiarity with Docker, Kubernetes, and MLOps practices for deploying AI models.
- Experience working with cloud-based AI services (Azure, AWS, or GCP).
- Strong problem-solving and debugging skills.
- Good to have experience with PyTorch, TensorFlow, Hugging Face Transformers.
