Senior Consultant - AI

Responsibilities

Technical Delivery

• Build and deliver AI/ML and data solutions on Azure with minimal oversight.
• Apply modern frameworks, tools, and engineering practices to design scalable solutions.
• Ensure solutions meet expectations for performance, reliability, security, and quality.

Client Engagement

• Contribute to discovery sessions and solution-design workshops with technical insight.
• Translate business requirements into actionable, well-structured technical components.
• Build trust with client stakeholders through thoughtful communication and high-quality delivery.

Project & Delivery Management

• Work independently within project teams to deliver assigned components on time and with quality.
• Collaborate with architects and project leads to address technical and delivery challenges.
• Support pre-sales and proposal development by providing technical expertise.

Leadership & People Development

• Share knowledge with peers and junior team members.
• Contribute to practice assets including best practices, frameworks, and accelerators.
• Stay current with emerging AI trends and proactively expand your skills.

Requirements

Education & Experience

• Bachelor's degree in Computer Science, Information Technology, or a related field preferred (or equivalent experience).
• 2–5 years of relevant experience with demonstrated ability to independently contribute across most areas of AI solution delivery.
• Prior consulting or client-facing experience preferred.

Proficiency across most of the following areas:

• Agents & Orchestration – leveraging Semantic Kernel, LangChain, or similar frameworks to design intelligent workflows; applying tool/function calling, planner and agent-based patterns, and workflow automation engines
• Search & Retrieval – implementing solutions with vector databases such as Azure AI Search, Pinecone, FAISS, or Milvus; applying hybrid search methods, re-ranking strategies, and selecting embedding models for accuracy and performance
• Evaluation, Quality & LLM Operations – establishing offline and online evaluation approaches; using golden datasets, hallucination and groundedness validation, tox