
Generative AI Model Specialist
1 day ago
Job Overview
We are seeking a highly skilled Generative AI Model Developer to join our team. This individual will be responsible for designing, developing, and deploying large language models and their associated applications.
Key Responsibilities
- LlM Development: Design, develop, and fine-tune Large Language Models (LLMs) using various frameworks such as GPT, LLaMA, Claude, and others.
- Prompt Engineering and Retrieval Augmented Generation Pipelines: Implement prompt engineering techniques, embeddings, and Retrieval Augmented Generation pipelines to enhance model performance.
- Agentic AI Applications Development: Develop Agentic AI applications using Langgraph and similar frameworks.
- API and Microservices Development: Design and implement APIs, microservices, and front-end/back-end integrations for Generative AI applications.
- Vector Database Integration: Work with vector databases such as Pinecone, FAISS, Weaviate, and Milvus to enable semantic search capabilities.
- Cloud AI Platform Deployment: Deploy solutions on cloud AI platforms such as Azure OpenAI, AWS Bedrock, and GCP Vertex AI.
- Model Optimization and Scalability: Optimize model performance, latency, and scalability for real-world usage.
- Collaboration and Responsible AI: Collaborate with data scientists and solution architects to deliver Proof-of-Concepts (PoCs) and production-ready applications, ensuring responsible AI, data privacy, and security guidelines are followed.
Required Skills and Qualifications
- Programming Skills: Strong programming skills in Python (preferably), with experience in APIs, microservices, Flask/FastAPI/Django.
- Azure Databricks and Apache Spark Experience: Hands-on experience with Azure Databricks and Apache Spark.
- Vector Embeddings and Vector Databases Knowledge: Familiarity with vector embeddings and vector databases.
- Orchestration Frameworks Experience: Experience with LangChain, LlamaIndex, or similar orchestration frameworks.
- Docker, Kubernetes, and MLOps Familiarity: Familiarity with Docker, Kubernetes, and MLOps practices for deploying AI models.
- Cloud-Based AI Services Experience: Experience working with cloud-based AI services (Azure, AWS, or GCP).
- Problem-Solving and Debugging Skills: Strong problem-solving and debugging skills.
- Additional Tools: Good knowledge of PyTorch, TensorFlow, Hugging Face Transformers is a plus.
What We Offer
We offer a dynamic work environment, competitive compensation package, and opportunities for growth and professional development.
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