Principal AI Engineer

eSimplicity•Columbia, MD

17d•$143,600 - $200,000•Remote

About The Position

The Agentic AI & MCP Specialist will architect, develop, and operationalize next-generation agentic systems powered by advanced LLMs and Model Context Protocol (MCP) frameworks. This role focuses on building intelligent, multi-step, tool-using agents that can autonomously reason, plan, and execute complex workflows across a cloud-based analytics ecosystem. The specialist will design and implement agent orchestration frameworks, integrate model-driven decision logic, and build robust, production-grade agent capabilities that safely leverage emerging AI techniques. This position requires a deeply skilled software developer who combines strong engineering fundamentals with hands-on experience creating agentic systems, working with MCP-based integrations, designing LLM-driven tools, and building secure, scalable AI applications. The role provides technical leadership, explores cutting-edge agentic patterns, drives proof-of-concept innovation, and partners with engineering and product teams to translate experimental architectures into real-world impact.

Requirements

Must pass public trust clearance through the U.S. Federal Government (requires U.S. citizenship or passing clearance through the Foreign National Government System, which includes living in the US for 3 of the last 5 years, having a valid passport, and appropriate VISA/work permit documentation).
Bachelor’s Degree and 10+ years of software engineering experience.
Experience designing, developing, and supporting production applications, platforms, or services.
Experience developing agentic AI solutions, including planning, tool utilization, workflow orchestration, multi-step reasoning, or autonomous task execution.
Experience designing and implementing Model Context Protocol (MCP) integrations, tool interfaces, or model-driven service architectures.
Ability to analyze business, customer, or mission requirements and develop scalable AI-driven solutions that align with technical and operational objectives.
Experience with large language model (LLM) development practices, including fine-tuning, retrieval-augmented generation (RAG), prompt engineering, and agent interaction patterns.
Proficiency in Python and experience working with APIs, microservices, distributed computing environments, and cloud-native architectures.
Experience deploying and integrating AI agents or LLM-enabled applications within cloud environments such as Azure, AWS, or Google Cloud Platform (GCP).
Knowledge of MLOps and LLMOps practices, including model versioning, automated testing, deployment automation, monitoring, performance evaluation, and governance.
Ability to contribute to solution design discussions, provide technical guidance to team members, and communicate AI-related concepts to technical and non-technical audiences.
Experience using version control systems and CI/CD practices, including source code management, automated testing, deployment pipelines, and release management for production environments.

Nice To Haves

Experience building multi-agent systems, agent swarms, or coordinated reasoning frameworks.
Familiarity with advanced tool-calling strategies, including dynamic tool selection, function-call planning, or graph-structured task planners.
Experience with structured LLM evaluation methods, agent benchmarking, or test harnesses for autonomous systems.
Knowledge of performance optimization techniques for LLMs and agents, including caching, model distillation, model routing, or accelerated inference.
Background integrating agentic components with large-scale data or analytics platforms (e.g., Databricks, Snowflake, Spark).
Hands-on experience developing innovative POCs or experimental agentic architectures in fast-paced R&D environments.
Familiarity with emerging agentic frameworks such as Strands Agents, LangGraph, CrewAI, etc.
Exposure to safety-oriented design patterns for autonomous systems, including guardrails, validation layers, or constrained-action frameworks.
Experience designing and building secure, compliance-aware systems that handle sensitive data in accordance with HIPAA and federal security standards, including implementation of encryption, access controls, auditability, and governance for protected health information (PHI) within AI/LLM workflows.

Responsibilities

Architect, develop, and operationalize next-generation agentic systems powered by advanced LLMs and Model Context Protocol (MCP) frameworks.
Build intelligent, multi-step, tool-using agents that can autonomously reason, plan, and execute complex workflows across a cloud-based analytics ecosystem.
Design and implement agent orchestration frameworks.
Integrate model-driven decision logic.
Build robust, production-grade agent capabilities that safely leverage emerging AI techniques.
Provide technical leadership.
Explore cutting-edge agentic patterns.
Drive proof-of-concept innovation.
Partner with engineering and product teams to translate experimental architectures into real-world impact.