Visionary Generative AI Developer

1 week ago

Mumbai, Maharashtra, India beBeeArtificial Full time US$ 2,00,000 - US$ 2,50,000
Generative AI Engineer

You will play a pivotal role in shaping the future of Generative AI and Large Language Models.

  • Key Responsibilities
  • Design, develop, and deploy cutting-edge AI/ML solutions leveraging frameworks such as LangChain or similar for building modular AI applications.
  • Fine-tune, prompt-engineer, and optimize LLMs for domain-specific tasks to achieve maximum accuracy and efficiency.
  • Integrate LLMs with APIs, databases, vector stores, and enterprise applications to ensure seamless deployment and scalability.
  • Evaluate and implement model deployment strategies (on-prem, cloud, serverless, or containerized environments) to meet business needs.
  • Collaborate with cross-functional teams to deliver end-to-end AI solutions, staying updated with the latest advancements in AI, ML, and GenAI research.
  • Implement MLOps best practices for continuous training, deployment, and monitoring of models in production.
  • Ensure responsible AI practices, focusing on bias mitigation, data privacy, and compliance to maintain a trustworthy AI ecosystem.

Required Skills

  • 5+ years of experience in software development (Python preferred)
  • 2+ years of hands-on experience in Generative AI and LLM-based development
  • Strong knowledge of AI/ML fundamentals including NLP, deep learning, embeddings, and transformers
  • Experience with LLMs (e.g., OpenAI GPT, Anthropic Claude, LLaMA, Falcon, Mistral, or similar)
  • Proven track record of delivering at least 2 production-grade LLM-based projects
  • Experience with LangChain, LlamaIndex, Haystack, or similar frameworks
  • Familiarity with vector databases (Pinecone, Weaviate, Milvus, FAISS, Chroma)
  • Hands-on experience with cloud platforms (AWS, Azure, GCP) and AI services (Bedrock, VertexAI, Azure OpenAI)
  • Proficiency in containerization and deployment tools (Docker, Kubernetes, CI/CD pipelines)
  • Experience with MLOps tools (MLflow, Kubeflow, Weights & Biases) for model lifecycle management
  • Strong problem-solving, analytical, and communication skills

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