Generative AI Engineer

Skill:

Preferred Location:

Experience Required:

Responsibilities:

· Design, build, and deploy generative AI solutions using LLMs such as OpenAI, Anthropic, Mistral, or open-source models (e.g., LLaMA, Falcon).

· Fine-tune and customize foundation models using domain-specific datasets and techniques.

· Develop and optimize prompt engineering strategies to drive accurate and context-aware model responses.

· Implement model pipelines using Python and ML frameworks such as PyTorch, Hugging Face Transformers, or LangChain.

· Collaborate with data engineers and MLOps teams to productionalize GenAI models on cloud platforms (Azure/AWS/GCP).

· Ensure robustness, scalability, and compliance of AI models in deployment environments.

· Good to have experience in finetuning models.

· Good to have experience in SLM.

· Integrate GenAI into enterprise applications via APIs or custom interfaces.

· Evaluate model performance using quantitative and qualitative metrics, and improve outputs through iterative experimentation.

· Keep up to date with the latest research in GenAI, foundation models, and relevant open-source tools.

Mandatory Skill Sets

· Generative AI (LLMs, Transformers)

· Python, PyTorch, Hugging Face Transformers

· Azure/AWS/GCP cloud platforms

· LangChain or similar orchestration frameworks

· REST APIs, FastAPI, Flask

· ML pipeline tools (MLFlow, Weights & Biases)

· Git CI/CD for ML (e.g., Azure ML, SageMaker pipelines)

Preferred Skill Sets

· RAG (Retrieval-Augmented Generation)

· Vector DBs (FAISS, Pinecone, Weaviate)

· Streamlit/Gradio for prototyping

· Docker, Kubernetes (for model deployment)

· Data preprocessing & feature engineering

· NLP libraries: spaCy, NLTK, Transformers