Senior AI Engineer - Agentic & Generative AI

About the Role:

We are looking for a seasoned Software Engineer with hands-on Agentic and Generative AI experience to provide technical leadership on client engagements. This is a hands-on role - you will design solutions, write code, and deliver alongside the team while also setting technical direction, mentoring engineers, and engaging directly with client stakeholders.

You will work heavily with AI coding agents to design and deliver solutions, which means strong prompt engineering discipline and the ability to direct AI-assisted development workflows are core to how this role operates. In a consulting environment, adaptability and the ability to bring structure to ambiguous problems are as important as technical depth.

Key Responsibilities:

Agentic AI Solution Design & Delivery

• Architect and deliver Agentic AI systems - multi-agent orchestration, tool use, memory, planning loops, and MCP integrations
• Design RAG pipelines and agentic workflows tailored to client knowledge and data landscapes
• Lead LLM integration strategy - model selection, prompt engineering, context management, and structured outputs across providers
• Use AI coding agents (Claude Code, Codex, Copilot, or equivalent) as a primary development tool; direct and validate AI-generated output with precision

Technical Leadership & Client Engagement

• Provide technical leadership on engagements - own solution design, drive requirements gathering and analysis, and guide the team through delivery
• Lead client discovery sessions, technical scoping, and solution presentations; translate ambiguous business problems into well-defined AI solutions
• Conduct design reviews, enforce engineering standards, and mentor junior and mid-level engineers
• Drive technology selection; establish reusable patterns and internal frameworks that improve delivery across the team

Data, Analytics & Engineering

• Apply statistical analysis and EDA to client data problems; generate charts, graphs, and visual summaries that communicate findings clearly to both technical and non-technical audiences
• Contribute to data quality assessment, pipeline design, and insight delivery using Python, numpy, and pandas
• Write clean, testable code following SOLID principles and design patterns; apply test-driven development practices and write unit tests as a standard part of delivery

Practice Development

• Stay current on the Agentic AI landscape and bring relevant advances into SPAR’s delivery practice
• Contribute to internal knowledge assets, accelerators, and capability building

Required

• 10+ years in software engineering with at least 1 year focused on Generative and Agentic AI
• Hands-on experience with at least one agentic framework - LangChain, LangGraph, AutoGen, CrewAI, or equivalent
• Direct experience prompting and integrating major LLMs - OpenAI, Anthropic Claude, Google Gemini, or similar
• Hands-on use of AI coding agents - Claude Code, OpenAI Codex, GitHub Copilot, or equivalent
• Experience with MCP integrations, agentic workflows, and multi-agent system design
• Strong prompt engineering skills - systematic, structured, and testable prompting practices
• Foundational understanding of ML concepts - how models are trained, data preparation, purpose and mechanics of fine-tuning, and when to apply pre-trained vs. fine-tuned models
• System design proficiency - distributed systems thinking, API design, scalability and reliability patterns
• Applied statistics and EDA - comfortable with data exploration, distributions, correlation, and translating findings into actionable insights
• Proficiency with Python, numpy, and pandas; experience with visualization libraries (matplotlib, seaborn, Plotly, or equivalent)
• Test-driven development practices and unit testing experience
• Experience leading technical workstreams or teams on client-facing consulting engagements
• Exceptional communication - equally effective whether writing a precise technical spec or presenting to a non-technical audience
• Agile methodology experience; comfortable owning delivery timelines and managing technical risk
• High adaptability - thrives across client domains and technology stacks; fast, self-directed learner

Desired:

• Conversational AI and chatbot design experience
• Hands-on experience with data analytics and data science tooling beyond EDA
• Deeper ML experience - has built, trained, and evaluated models beyond conc…