Artificial Intelligence Engineer

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 productionize 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