Consultant/Sr. Consultant - AI Engineer (INAI)

• Master’s degree or relevant degree/certification in quantitative discipline, e.g., Computer Science, Mathematics, Statistics, Artificial Intelligence.
• Hands-on experience with statistical software tools. We prefer experience in Python and Python statistical libraries. Experience in R is also accepted, as is SAS, SPSS, Strata, and MATLAB.
• Deep conceptual understanding of probability & statistics, ML algorithm intuition, and computer science fundamentals.
• Deep experience in statistical and machine learning techniques such as classification, regression, feature selection and feature engineering, hyperparameter tuning, unsupervised learning methods, time series analysis, forecasting etc.
• Deep understanding of GenAI Models training and fine tuning of them.
• Proven experience in Generative AI (GenAI) including model training, fine-tuning, and deployment.
• Practical expertise with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Claude, etc.
• Experience implementing Retrieval-Augmented Generation (RAG) pipelines using Vector Databases (e.g., Pinecone, Weaviate, Milvus, FAISS).
• Strong knowledge of Embeddings (text, image, multimodal) and their application in semantic search, personalization, and recommendation systems.
• Familiarity with Agentic AI frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) for building autonomous and multi-agent systems.

• Research, design, and implement GenAI-powered solutions for complex business problems.
• Build and deploy LLM-based applications including chatbots, copilots, and autonomous agents.
• Architect and optimize RAG pipelines for enterprise knowledge retrieval and augmentation.
• Develop and evaluate embedding-based solutions for semantic search, personalization, and recommendation.
• Experiment with agentic AI frameworks to design multi-agent workflows and autonomous decision-making systems.
• Research machine learning algorithms develop solution formulations, and test on large datasets.
• Given unstructured and complex business problems, design and develop tailored analytic solutions.
• Design experiments, test hypotheses, and build actionable models.
• Solve analytical problems and effectively communicate methodologies and results.
• Draw relevant inferences and insights from data including identification of trends and anomalies.
• Translate unstructured, complex business problems into abstract mathematical frameworks, making intelligent analogies and approximations to produce working algorithms at scale.

• An analytical mind with problem-solving abilities
• Ability to design and optimize RAG pipelines for enterprise-scale knowledge management.
• Understanding of text representation techniques (BERT, ELMo, etc.) and statistics
• Deep understanding of the LSTM/CNN functionality and architecture
• Experience with information extraction and retrieval techniques (e.g. Named Entity Recognition, Dependency Parsing, Coreference Resolution, etc.)
• Hands-on with text mining and NLP libraries (SpaCy, NLTK, Hugging Face, OpenAI APIs).
• Experience with any cloud DS/AI platforms (e.g. AWS Sage maker, Azure Machine Learning, etc.) will be a bonus.
• Knowledge of multimodal AI (text, vision, speech) and emerging techniques in agentic workflows.
• Experience with less common techniques, such as probabilistic graphical models, generative algorithms, genetic algorithms, reinforcement learning, etc.
• Personal projects and Kaggle competition results can serve as differentiation.

• Strong interpersonal and communication skills.
• Ability to explain statistical reasoning to both experts and non-experts.
• Strong communication and interpersonal skills.
• Ability to learn new skills/technologies quickly and independently.
• Independent problem-solving skills.

Blue Altair