Generative AI (Minimum Requirements):

• Strong understanding of LLMs, agent architectures, generative pipelines.

• Proven experience in delivering AI solutions in enterprise setting.

• Hands-on with backend engineering: Python/Node.js, &REST APIs.

• Familiar with prompt engineering, model fine-tuning, and retrieval-augmented generation (RAG)

• Excellent communication skills with business and technical stakeholders

• Hands-on experience with LangGraph or similar AI agent frameworks

• Proficiency with LlamaIndex, LangChain, or equivalent data processing libraries

• Experience with vector databases (Pinecone, Weaviate, Chroma, etc.)

• Working knowledge of multiple LLMs and their APIs (OpenAI GPT, Gemini, Claude)

• Experience with LLM observability tools (LangSmith, Weights & Biases, etc.)

• Experience developing RAG (Retrieval-Augmented Generation) chatbots

• Experience with coding agents and code generation systems

Traditional AI/ML:

• Strong foundation in clustering, regression, classification, and forecasting

• Proficiency with scikit-learn, PyTorch, TensorFlow

• Experience with statistical analysis and experimental design

• Knowledge of feature engineering and data preprocessing techniques

• Understanding of multimodal AI.

Good to Have:

• Experience onAWS Bedrock/SageMaker

• Fine-tuning experience with LLMs, rerankers, or embedding models

• Self-hosting and deployment of open-source LLMs

• Experience with BERT, transformer architectures, or computer vision models (YOLO)

• MLOps experience with MLflow, Weights & Biases, or TensorBoard

• AWS Cloud platform certifications

• Fine-tuning tools: LoRA, QLoRA, PEFT

• Experience in building LLM BI solutions (natural language to SQL)

5. Nice to have requirements to the candidate

• Prior experience working on projects with a lot of PII data or working in Financial Services industry is a "Plus".

• Experience with multimodal AI systems combining text, image, and speech

• Familiarity with AI Ethics, Safety, and Responsible AI Frameworks

• Contributions to open-source AI projects or community involvement

Essential functions

Generative AI (Minimum Requirements):

• Strong understanding of LLMs, agent architectures, generative pipelines.

• Proven experience in delivering AI solutions in enterprise setting.

• Hands-on with backend engineering: Python/Node.js, &REST APIs.

• Familiar with prompt engineering, model fine-tuning, and retrieval-augmented generation (RAG)

• Excellent communication skills with business and technical stakeholders

• Hands-on experience with LangGraph or similar AI agent frameworks

• Proficiency with LlamaIndex, LangChain, or equivalent data processing libraries

• Experience with vector databases (Pinecone, Weaviate, Chroma, etc.)

• Working knowledge of multiple LLMs and their APIs (OpenAI GPT, Gemini, Claude)

• Experience with LLM observability tools (LangSmith, Weights & Biases, etc.)

• Experience developing RAG (Retrieval-Augmented Generation) chatbots

• Experience with coding agents and code generation systems

Traditional AI/ML:

• Strong foundation in clustering, regression, classification, and forecasting

• Proficiency with scikit-learn, PyTorch, TensorFlow

• Experience with statistical analysis and experimental design

• Knowledge of feature engineering and data preprocessing techniques

• Understanding of multimodal AI.

Qualifications

Traditional AI/ML:

• Strong foundation in clustering, regression, classification, and forecasting

• Proficiency with scikit-learn, PyTorch, TensorFlow

• E Official notification