Generative AI Engineer
7 days ago
Role Overview
We seek a motivated Junior 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.
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Key Responsibilities
1. Model Development & Fine-Tuning
o Assist in developing, training, and fine-tuning generative models (text, image, code) using frameworks like PyTorch, TensorFlow, or JAX.
o Implement RAG (Retrieval-Augmented Generation) pipelines and optimize prompts for specific domains.
2. Tooling & Integration
o Build applications using tools like LangChain, LlamaIndex, or Hugging Face Transformers.
o Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enter-prise workflows.
3. Prompt Engineering
o Design and test advanced prompting strategies (e.g., few-shot learning, chain-of-thought, ReAct frameworks) for domain-specific tasks (legal, healthcare, finance).
o Create reusable prompt templates for common workflows (customer support, code generation, content moderation).
4. Evaluation & Optimization
o Develop metrics for hallucination reduction, output con-sistency, and safety alignment.
o Optimize model inference costs using quantization, distilla-tion, or speculative decoding.
5. Collaboration
o Work with cross-functional teams (product, data engineers, UX) to deploy AI solutions.
o Document technical processes and contribute to knowledge-sharing sessions.
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Qualifications
· Education: Bachelor’s/Master’s in Computer Science, Data Science, or related field.
· Technical Skills:
o Proficiency in Python and familiarity with AI/ML libraries (PyTorch, TensorFlow).
o Basic understanding of NLP (tokenization, attention mecha-nisms) and neural architectures (Transformers, GANs).
o Experience with cloud platforms (AWS SageMaker, GCP Ver-tex AI, Azure ML).
o Proficiency in prompt engineering tools: LangChain, DSPy, Guidance, or LMQL.
o Experience with AI deployment tools: FastAPI, Docker, or MLflow for model serving
· AI/GenAI Exposure and experience with at least two of the follow-ing:
o Hands-on projects with LLMs (fine-tuning, prompt engineer-ing) or diffusion models.
o Familiarity with vector databases (Pinecone, Milvus) and or-chestration tools.
o Fine-tuning/training LLMs (e.g., Llama 2, Mistral) using LoRA, QLoRA, or RLHF.
o Building RAG pipelines with vector DBs (Pinecone, Weaviate) and embedding models (BERT, OpenAI text-embedding).
o Developing applications with diffusion models (Stable Diffusion, DALL-E) or autoregressive architectures (GPT variants).
o Contributions to NLP projects (sentiment analysis, NER, text summa-rization) using libraries like spaCy or NLTK.
· Soft Skills:
o Strong problem-solving abilities and curiosity about emerging AI trends.
o Ability to communicate technical concepts to non-technical stakeholders.
Preferred Qualifications Additions
· Certifications:
o Azure: Microsoft Certified: Azure AI Engineer Associate.
o GCP: Google Cloud Professional Machine Learning Engineer
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