Generative AI Engineer

Role OverviewWe seek a motivated Generative AI Developer to design, implement, and optimize cutting-edge generative AI solutions. You’ll work closely with senior engineers to build applications leveraging LLMs (e.g., GPT-4, Claude, Gemini), diffusion models, and multimodal systems while adhering to ethical AI practices. This will be a hands-on individual contributor role.Key ResponsibilitiesModel Development & Fine-TuningAssist 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 & IntegrationBuild applications using tools like LangChain, LlamaIndex, or Hugging Face Transformers.Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enterprise workflows.Prompt EngineeringDesign 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 & OptimizationDevelop metrics for hallucination reduction, output consistency, and safety alignment.Optimize model inference costs using quantization, distillation, or speculative decoding.CollaborationWork with cross-functional teams (product, data engineers, UX) to deploy AI solutions.Document technical processes and contribute to knowledge-sharing sessions.QualificationsEducation: 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 servingAI/GenAI 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 AdditionsCertifications:Azure: Microsoft Certified: Azure AI Engineer Associate.GCP: Google Cloud Professional Machine Learning Engineer.

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