Generative AI Engineer

As a Generative AI Developer, you will be responsible for designing, implementing, and optimizing cutting-edge generative AI solutions. You will collaborate closely with senior engineers to build applications utilizing LLMs (e.g., GPT-4, Claude, Gemini), diffusion models, and multimodal systems while upholding ethical AI practices. This role will require hands-on individual contribution. Key Responsibilities: - Model Development & Fine-Tuning: - Assist in developing, training, and fine-tuning generative models (text, image, code) using frameworks like PyTorch, TensorFlow, or JAX. - Implement RAG (Retrieval-Augmented Generation) pipelines and optimize prompts for specific domains. - Tooling & Integration: - Build applications using tools like LangChain, LlamaIndex, or Hugging Face Transformers. - Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enterprise workflows. - Prompt Engineering: - Design and test advanced prompting strategies (e.g., few-shot learning, chain-of-thought, ReAct frameworks) for domain-specific tasks (legal, healthcare, finance). - Create reusable prompt templates for common workflows (customer support, code generation, content moderation). - Evaluation & Optimization: - Develop metrics for hallucination reduction, output consistency, and safety alignment. - Optimize model inference costs using quantization, distillation, or speculative decoding. - Collaboration: - Work with cross-functional teams (product, data engineers, UX) to deploy AI solutions. - Document technical processes and contribute to knowledge-sharing sessions. Qualifications: - Education: - Bachelor's/Master's in Computer Science, Data Science, or related field. - Technical Skills: - Proficiency in Python and familiarity with AI/ML libraries (PyTorch, TensorFlow). - Basic understanding of NLP (tokenization, attention mechanisms) and neural architectures (Transformers, GANs). - Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML). - Proficiency in prompt engineering tools: LangChain, DSPy, Guidance, or LMQL. - Experience with AI deployment tools: FastAPI, Docker, or MLflow for model serving. - Exposure and experience with at least two of the following: - Hands-on projects with LLMs (fine-tuning, prompt engineering) or diffusion models. - Familiarity with vector databases (Pinecone, Milvus) and orchestration tools. - Fine-tuning/training LLMs (e.g., Llama 2, Mistral) using LoRA, QLoRA, or RLHF. - Building RAG pipelines with vector DBs (Pinecone, Weaviate) and embedding models (BERT, OpenAI text-embedding). - Developing applications with diffusion models (Stable Diffusion, DALL-E) or autoregressive architectures (GPT variants). - Contributions to NLP projects (sentiment analysis, NER, text summarization) using libraries like spaCy or NLTK. - Soft Skills: - Strong problem-solving abilities and curiosity about emerging AI trends. - Ability to communicate technical concepts to non-technical stakeholders. Preferred Qualifications Additions: - Certifications: - Azure: Microsoft Certified: Azure AI Engineer Associate. - GCP: Google Cloud Professional Machine Learning Engineer. As a Generative AI Developer, you will be responsible for designing, implementing, and optimizing cutting-edge generative AI solutions. You will collaborate closely with senior engineers to build applications utilizing LLMs (e.g., GPT-4, Claude, Gemini), diffusion models, and multimodal systems while upholding ethical AI practices. This role will require hands-on individual contribution. Key Responsibilities: - Model Development & Fine-Tuning: - Assist in developing, training, and fine-tuning generative models (text, image, code) using frameworks like PyTorch, TensorFlow, or JAX. - Implement RAG (Retrieval-Augmented Generation) pipelines and optimize prompts for specific domains. - Tooling & Integration: - Build applications using tools like LangChain, LlamaIndex, or Hugging Face Transformers. - Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enterprise workflows. - Prompt Engineering: - Design and test advanced prompting strategies (e.g., few-shot learning, chain-of-thought, ReAct frameworks) for domain-specific tasks (legal, healthcare, finance). - Create reusable prompt templates for common workflows (customer support, code generation, content moderation). - Evaluation & Optimization: - Develop metrics for hallucination reduction, output consistency, and safety alignment. - Optimize model inference costs using quantization, distillation, or speculative decoding. - Collaboration: - Work with cross-functional teams (product, data engineers, UX) to deploy AI solutions. - Document technical processes and contribute to knowledge-sharing sessions. Qualifications: - Education: - Bachelor's/Master's in Computer Science, Data Science, or relate