AI Engineer

Platinum Technologies•Washington, DC

About The Position

Platinum Technologies is seeking an AI Engineer to join our company. We are looking for an AI Engineer who thinks in systems, not products. You will design, build, and operate intelligent AI solutions for Federal Government clients, solutions that work today and are portable tomorrow. The right candidate is equally comfortable architecting a RAG pipeline on Azure as they are lifting and shifting an AI agent to AWS GovCloud or an on-premises environment. Designing and developing products that are robust, modular, cloud-agnostic AI is the mission for this role. This is not a single-cloud role. You will be expected to design AI systems that are platform-portable by default, choosing the right model, the right infrastructure, and the right integration pattern for each mission context, not the one your cloud vendor recommends.

Requirements

Bachelor’s degree in Computer Science or a related field.
Minimum five (5+) years of experience, with 3–5 years specialized in AI development.
Ability to obtain and maintain a Public Trust clearance.
Green Card holders must have 3 years of U.S. residency for ITA eligibility.

Nice To Haves

AWS Certified AI Practitioner.
Microsoft Certified: Azure AI Engineer Associate.

Responsibilities

Architect and deliver end-to-end AI solutions — RAG pipelines, agentic workflows, multi-modal applications, that are modular, interoperable, and designed for portability across cloud providers and on-premises environments.
Lead LLM-agnostic design decisions: evaluate and integrate models from OpenAI, Anthropic, Meta (Llama), Mistral, Cohere, and open-source alternatives based on mission fit, not vendor preference.
Design and implement AI agents and multi-agent orchestration using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or custom implementations, with clear separation of concerns to enable future platform migration.
Build and optimize retrieval systems using vector databases (Pinecone, Weaviate, pgvector, OpenSearch) and hybrid search strategies not tied to a single managed service.
Own the full AI lifecycle: data ingestion, embedding strategy, chunking, prompt engineering, evaluation, fine-tuning (LoRA/PEFT), and continuous monitoring.
Design infrastructure using IaC (Terraform, Pulumi) with multi-cloud support across Azure (OpenAI, AI Search, AKS), AWS (Bedrock, SageMaker), and GCP (Vertex AI) — enabling lift-and-shift without architectural rework.
Apply semantic chunking, knowledge graph integration, and structured data fusion to improve retrieval fidelity in complex government data environments.
Implement robust evaluation frameworks (RAGAS, LLM-as-judge, human-in-the-loop review) to measure and improve AI system quality continuously.
Engage clients directly whiteboard sessions, solution design reviews, and stakeholder presentations translating complex AI architecture into clear mission outcomes.
Ensure all AI solutions comply with federal standards (FedRAMP, FISMA, NIST AI RMF, NIST 800-53) and apply responsible AI practices including bias detection and model explainability.