Generative AI Developer

Role & responsibilities

• 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.
• Build applications using tools like LangChain, LlamaIndex, or Hugging Face Transformers.
• Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enterprise workflows.
• 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).

Preferred candidate profile

• Education

  : Bachelors/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

• AI/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.