AI/ML Platform Engineer

🔥 4 minutes ago

⚔️ Virginia – Remote

⚔️ Virginia – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🏗️ Platform Engineer

👻 Ghost score 10%

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SAIC

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1969

☁️ SaaS

📣 Marketing

🏢 Enterprise

💰 $500M Post-IPO Debt - SAIC on 2025-09

SaaS • Marketing • Enterprise

SAIC is represented in the supplied content by marketing and product copy that matches Bitly — a cloud-based link management and QR-code platform. The material describes a SaaS offering for URL shortening, dynamic QR Codes, link analytics, mobile-friendly landing pages, branded links, UTM campaign tracking, digital business cards, developer APIs and AI-enhanced features. It targets marketers, developers, and enterprises across industries (retail, CPG, hospitality, media, healthcare, finance, education) and emphasizes integrations, analytics, and enterprise-grade capabilities. Note: the provided text appears to be Bitly’s website content rather than information about SAIC; if you intended the defense/technology firm SAIC (Science Applications International Corporation), please provide relevant content.

📋 Description

• Enable AI/ML platform services as a governed, multi-tenant enterprise offering across traditional ML, GenAI, and agentic AI. • Provide governed access to frontier models through an AI gateway layer, RAG infrastructure, knowledge bases, vector databases, embedding pipelines, per-tenant guardrails, grounding validation, and feedback mechanisms. • Enable agentic AI patterns including orchestration frameworks, tool/API integration, memory and state management, human-in-the-loop controls, and safety guardrails. • Enable traditional ML operationalization through governed training environments, model deployment pipelines with TAI controls, and fleet-wide drift and quality monitoring. • Partner directly with customers from concept through production handoff, owning requirements, specifications, solution design, implementation, and delivery. • Evaluate and integrate new AI/ML platform capabilities, tools, and services. • Build repeatable and portable platform onboarding patterns and IaC templates using Terraform and CloudFormation, with SSO integration. • Develop secure API-based model access and inference endpoints consistent with governance and security standards. • Document reusable patterns, frameworks, and APIs for future deployments.

🎯 Requirements

• Bachelor’s or master’s degree in computer science, Machine Learning, or a related field. • 5+ years of current, hands-on AI/ML engineering experience actively writing code, building pipelines, and architecting systems across traditional ML, GenAI, and/or agentic AI. • Demonstrated platform-level thinking and experience enabling AI/ML capabilities as a managed service, including governed multi-tenant compute, model deployment pipelines with guardrails, GenAI access layers, or agentic orchestration infrastructure. • Strong proficiency in Python and hands-on experience with PyTorch, TensorFlow, HuggingFace, LangChain, or equivalent. • Hands-on experience with cloud-based AI/ML platforms such as Databricks, SageMaker, or equivalent, including model development, MLOps, data governance, and multi-tenant operationalization. • Hands-on experience with GenAI and LLM frameworks, RAG pipelines, vector databases, embedding models, prompt engineering, and governed model access layers. • Strong IaC and cloud engineering skills with Terraform, CloudFormation, AWS-native services, enterprise SSO integration, and cloud security patterns. • Knowledge of federal security and governance standards, including FedRAMP, NIST 800-53, and data isolation best practices. • Proven ability to work directly with customers and translate ambiguous problem statements into requirements, specifications, and working solutions end to end. • Comfortable working in regulated environments with governance requirements and security standards. • Desired: experience with agentic AI frameworks, AI gateway patterns such as LiteLLM, vector databases including Pinecone, FAISS, Weaviate, or Amazon OpenSearch Vector, CI/CD and MLOps practices including MLflow, Git, and Docker, and regulated-industry experience.