Generative AI Developer
3 days ago
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: Bachelors/Masters 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
AI/GenAI Exposure and experience
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