Job Description: Summary:
The Agentic AI & MCP Specialist will architect, develop, and operationalize nextgeneration agentic systems powered by advanced LLMs and Model Context Protocol (MCP) frameworks. This role focuses on building intelligent, multistep, toolusing agents that can autonomously reason, plan, and execute complex workflows across a cloudbased analytics ecosystem. The specialist will design and implement agent orchestration frameworks, integrate modeldriven decision logic, and build robust, productiongrade agent capabilities that safely leverage emerging AI techniques.
This position requires a deeply skilled software developer who combines strong engineering fundamentals with handson experience creating agentic systems, working with MCPbased integrations, designing LLMdriven tools, and building secure, scalable AI applications. The role provides technical leadership, explores cuttingedge agentic patterns, drives proofofconcept innovation, and partners with engineering and product teams to translate experimental architectures into realworld impact. Must Have Requirements:
Strong software engineering background with deep experience building productiongrade applications and services.
Expertise developing agentic AI systems, including planning, tooluse, multistep reasoning, workflow execution, or autonomous decisioning logic.
Handson experience designing and implementing MCPbased integrations, tool interfaces, or modeldriven service frameworks.
Ability to translate ambiguous business or mission requirements into scalable AI-driven solutions, balancing technical feasibility with real-world impact.
Proficiency with LLM development practices including finetuning, RAG integration, prompt engineering, and interaction models for agent workflows.
Strong Python development skills and familiarity with distributed compute environments, APIs, microservices, and cloudnative architectures.
Experience integrating agents or LLMdriven components into cloud platforms (Azure, AWS, Google Cloud Platform) or largescale data ecosystems.
Understanding of LLMOps/MLOps principles including versioning, testing, deployment automation, monitoring, and governance for agentic systems.
Demonstrated ability to lead solution design, mentor developers, and communicate complex AI architectures to technical and nontechnical stakeholders.
Experience with version control and modern CI/CD practices (e.g., Git/GitHub), including automated testing, deployment pipelines, and release management for production systems.
Ability to obtain/maintain a Public Trust clearance. Preferred Requirements:
Experience building multiagent systems, agent swarms, or coordinated reasoning frameworks.
Familiarity with advanced toolcalling strategies, including dynamic tool selection, functioncall planning, or graphstructured 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 largescale data or analytics platforms (e.g., Databricks, Snowflake, Spark).
Handson experience developing innovative POCs or experimental agentic architectures in fastpaced R&D environments.
Familiarity with emerging agentic frameworks such as Strands Agents, LangGraph, CrewAI, etc.
Exposure to safetyoriented design patterns for autonomous systems, including guardrails, validation layers, or constrainedaction 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. Education/Experience:
Bachelor's degree in computer science, software engineering or relevant field required.
10+ years experience required.
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