Creative AI Developer

2 days ago

Dombivli, Maharashtra, India beBeeArtificial Full time ₹ 15,00,000 - ₹ 25,00,000

Generative AI Engineer Job Description

We are seeking a highly skilled Generative AI Engineer to join our organization. As a key member of our team, you will be responsible for developing and integrating Large Language Models (LLMs) into enterprise applications.

Key Responsibilities:

  • Develop and Integrate LLMs: Develop, fine-tune, and integrate LLMs such as GPT, LLaMA, and Claude into our enterprise applications.
  • Prompt Engineering and RAG Pipelines: Implement prompt engineering techniques, embeddings, and Retrieval Augmented Generation (RAG) pipelines to enhance model performance.
  • Agentic AI Applications: Understand and build Agentic AI applications using Langgraph and similar technologies.
  • API Development and Integration: Build and maintain APIs, microservices, and front-end/back-end integrations for Generative AI applications.
  • Vector Database Integration: Work with vector databases like Pinecone, FAISS, Weaviate, and Milvus to enable semantic search in our applications.
  • Cloud Deployment: Deploy solutions on cloud AI platforms such as Azure OpenAI, AWS Bedrock, and GCP Vertex AI.
  • Model Optimization: Optimize model performance, latency, and scalability for real-world usage.
  • Collaboration and Quality Assurance: Collaborate with data scientists and solution architects to deliver Proof-of-Concepts (PoCs) and production-ready applications, ensuring that our AI solutions follow responsible AI, data privacy, and security guidelines.

Required Skills and Qualifications:

  • Strong Programming Skills: Strong programming skills in Python (preferred), with experience in APIs, microservices, Flask/FastAPI/Django.
  • Azure Databricks and Apache Spark: Hands-on experience with Azure Databricks and Apache Spark.
  • Vector Embeddings and Databases: Knowledge of vector embeddings and vector databases.
  • LangChain and Similar Frameworks: Experience with LangChain, LlamaIndex, or similar orchestration frameworks.
  • Docker, Kubernetes, and MLOps: Familiarity with Docker, Kubernetes, and MLOps practices for deploying AI models.
  • Cloud-Based AI Services: Experience working with cloud-based AI services such as Azure, AWS, or GCP.
  • Problem-Solving and Debugging: Strong problem-solving and debugging skills.
  • Additional Skills: Good to have experience with PyTorch, TensorFlow, Hugging Face Transformers.

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