Artificial Intelligence Consultant

Short Description

Roles and Responsibilities

Thought Leadership: Conduct market research, stay updated with the latest in GenAI and RAG/Agentic systems, and contribute to whitepapers, blogs, and new offerings.

AI Engagements: Independently manage end-to-end delivery of AI-led transformation projects across industries, ensuring value realization and high client satisfaction.

Strategic Consulting & Roadmapping: Identify key enterprise challenges and translate them into AI solution opportunities, crafting transformation roadmaps that leverage RAG, LLMs, and intelligent agent frameworks.

LLM/RAG Solution Design & Implementation: Architect and deliver cutting-edge AI systems using Python, LangChain, LlamaIndex, OpenAI function calling, semantic search, and vector store integrations (FAISS, Qdrant, Pinecone, ChromaDB).

Agentic Systems: Design and deploy multi-step agent workflows using frameworks like CrewAI, LangGraph, AutoGen or ReAct, optimizing tool-augmented reasoning pipelines.

Client Engagement & Advisory: Build lasting client relationships as a trusted AI advisor, delivering technical insight and strategic direction on generative AI initiatives.

Hands-on Prototyping: Rapidly prototype PoCs using Python and modern ML/LLM stacks to demonstrate feasibility and business impact.

Essential Skills

Leadership Quality: Proven track record in leading cross-functional teams and delivering enterprise-grade AI projects with tangible business impact.

Business Consulting Mindset: Strong problem-solving, stakeholder communication, and business analysis skills to bridge technical and business domains.

Python & AI Proficiency: Advanced proficiency in Python and popular AI/ML libraries (e.g., scikit-learn, PyTorch, TensorFlow, spaCy, NLTK). Solid understanding of NLP, embeddings, semantic search, and transformer models.

LLM Ecosystem Fluency: Experience with OpenAI, Cohere, Hugging Face models; prompt engineering; tool/function calling; and structured task orchestration.

Independent Contributor: Ability to own initiatives end-to-end, take decisions independently, and operate in fast-paced environments.

Preferred Skills

Cloud Platform Expertise: Strong familiarity with Microsoft Azure (preferred), AWS, or GCP including compute instances, storage, managed services, and serverless/cloud- native deployment models.

Programming Paradigms: Hands-on experience with both functional and object-oriented programming in AI system design.

Hugging Face Ecosystem: Proficiency in using Hugging Face Transformers, Datasets, and Model Hub.

Vector Store Experience: Hands-on experience with FAISS, Qdrant, Pinecone, ChromaDB.

LangChain Expertise: Strong proficiency in LangChain for agentic task orchestration and RAG pipelines.

MLOps & Deployment: CI/CD for ML pipelines, MLOps tools (MLflow, Azure ML), containerization (Docker/Kubernetes).

Cloud & Service Architecture: Knowledge of microservices, scaling strategies, inter- service communication.

Programming Languages: Proficiency in Python and C# for enterprise-grade AI solution development.

Education:

Experience: Minimum 5 years of experience in consulting or technology roles, with at least 3 years focused on AI & ML solutions.