Agentic AI Engineer

Illumination Works LLC• US,

1d•Remote

About The Position

Join our Data Science Team! Our collaborative and diverse data science team is focused on helping organizations unlock business value through machine learning, analytics, generative AI, agentic AI systems, and intelligent automation. We work across industries including sustainment, logistics, industrial modernization, acquisition, cybersecurity, retail, and manufacturing to design and implement solutions that solve complex business challenges. Our projects range from creating analytic insights from data to enterprise-level end-to-end knowledge engines supporting application development. We apply natural language processing (NLP), computer vision, sensor analytics, knowledge graphs, large language models (LLMs), retrieval augmented generation (RAG), agentic workflows, statistical modeling, visualization, and human-in-the-loop decision systems to provide world class solutions. We are seeking an experienced Agentic AI Engineer who is passionate about emerging AI technologies and can bridge the gap between business objectives and practical AI implementation. The ideal candidate will bring deep technical expertise in LLMs, agentic architectures, and modern AI frameworks while helping clients identify high-value use cases and implement scalable, production-ready AI solutions. Do you have what it takes? Are you driven to solve complex business problems with cutting-edge AI technologies? Do you enjoy experimenting with emerging frameworks, evaluating new models, and designing intelligent systems that create measurable impact? We value curiosity, innovation, collaboration, and the ability to communicate technical concepts to diverse audiences.

Requirements

Strong proficiency in Python and experience developing AI applications using modern AI and machine learning frameworks
Hands-on experience with LLM orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or similar technologies
Experience with vector databases and retrieval technologies
Experience implementing APIs, microservices, and cloud-native AI solutions
Knowledge of prompt engineering, model fine-tuning, evaluation frameworks, and AI safety considerations
Strong problem-solving, systems-thinking, and analytical skills
Ability to identify and mitigate AI bottlenecks, optimize system latency and increase traceability and confidence when building on very large data lake architectures
Excellent verbal and written communication skills
B.S. in Computer Science, Information Technology, Statistics, Analytics, Mathematics, Engineering or related scientific field.
Minimum of seven (7) years of experience performing data science in corporate setting
Minimum of two (2) years of hands-on experience building and deploying AI or machine learning solutions
Real world experience deploying production-level agentic workflows
Must have or be willing to obtain Secret Clearance (this requires US Citizenship)
Acceptable candidates must successfully pass a drug test and background screen

Nice To Haves

Highly collaborative and skilled at translating technical topics to non-technical users
Experience with fine-tuning open-source models and running AI workloads on private infrastructure
Experience with MLOps, LLMOps, and AI lifecycle management practices
M.S. and/or Ph.D. in engineering, computer science, or related scientific discipline preferred

Responsibilities

Design, develop, and deploy AI solutions leveraging LLMs, RAG, and agentic AI architectures
Design, evaluate, and maintain a seamless generative pipeline that accepts user queries/prompts in conversational language, handles real-time prompt orchestration to generate safe SQL, extracts required insights, and reformats raw datasets into clean, actionable, human-readable answers
Evaluate business processes and identify opportunities where generative AI and autonomous agents can improve efficiency, decision-making, and customer outcomes
Architect multi-agent and agentic workflows using modern frameworks
Develop AI systems that effectively utilize tools, APIs, enterprise data sources, and external services to perform complex tasks
Implement prompt engineering, model evaluation, guardrails, observability, and governance practices for production AI applications
Build and optimize RAG pipelines, vector databases, knowledge retrieval systems, and semantic search capabilities
Assess emerging AI models and technologies to determine the most effective and cost-efficient solutions for client needs
Integrate AI capabilities into enterprise software ecosystems, cloud platforms, and business workflows
Design and implement AI monitoring, testing, evaluation, and continuous improvement processes
Collaborate with business stakeholders, solution architects, software engineers, and data scientists to deliver AI-driven solutions
Present AI concepts, recommendations, architectures, and implementation strategies to technical and executive audiences