Senior AI Engineer - Agentic & Generative AI

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 .