Artificial Intelligence Engineer

A private equity–backed operating platform is hiring Forward Deployed AI Platform Engineersdesign, build, and scale next-generation agentic AI systems across its portfolio companies. This is a highly technical, hands-on role for engineers who can operate across architecture, infrastructure, ML systems, and executive-level delivery. Engineers in this role act as embedded technical owners—building production AI platforms, defining architecture standards, and driving adoption of generative AI across enterprise environments. What You’ll Do Serve as a forward-deployed AI platform lead across multiple portfolio companies, owning end-to-end design and delivery of production AI systems Architect and build agentic AI platforms using multi-agent systems, RAG pipelines, and LLM orchestration frameworks Design and deploy enterprise-grade GenAI systems using AWS and Azure, including GovCloud and regulated environments where applicable Build scalable backend systems including: Microservices architectures API-driven AI services Event-driven and distributed systems Lead development of LLM-powered applications, including: Conversational analytics over enterprise data (e.g., OpenSearch + vector search + embeddings) Synthetic data generation pipelines for model training and testing Workflow automation using multi-agent orchestration systems Own cloud infrastructure and DevOps pipelines, including: VPC design, Lambda-based architectures, CI/CD pipelines Infrastructure-as-code using Terraform / OpenTofu Containerization (ECR, Docker, distributed deployments) Lead AI model evaluation, benchmarking, and optimization, including tradeoffs across latency, cost, and performance across models (Claude, GPT-4o, Bedrock models) Implement AI governance, guardrails, and responsible AI frameworks, including red-teaming, prompt injection defense, and data leakage prevention Partner with portfolio company executives to: Run AI strategy workshops Define roadmap for AI adoption Translate business problems into scalable technical systems Support GTM and innovation efforts by building AI accelerators, demos, and reusable platform components Collaborate with hyperscalers and ecosystem partners (AWS, OpenAI, Anthropic, LangChain ecosystem) to drive new AI capabilities into production What We’re Looking For 5–10+ years of experience in AI engineering, platform engineering, or advanced software engineering roles Deep hands-on experience building production GenAI or ML systems (not prototypes only) Strong Python engineering background with experience in: Backend systems / microservices API development and distributed systems Experience with agentic AI and LLM frameworks, including: LangChain, multi-agent architectures, or similar orchestration systems RAG pipelines and semantic search systems Strong cloud engineering experience in: AWS (including Lambda, VPC, OpenSearch, ECR) Azure (including Azure Government or AI services preferred) Experience working with LLM platforms and models, including: Amazon Bedrock (Claude, Titan embeddings, etc.) OpenAI GPT-4o or similar frontier models Strong understanding of: Vector databases and semantic retrieval systems Model evaluation, benchmarking, and performance tuning Data pipelines and AI system architecture Experience operating in regulated or high-security environments (FedRAMP, GovCloud, financial services, or similar strongly preferred) Strong communication skills with ability to operate across: Engineering teams Executive stakeholders External partners and vendors Nice to Have Experience with synthetic data generation and secure ML pipelines Exposure to AI governance boards, compliance frameworks, or responsible AI programs Experience with tools like: Arize (LLM observability / evaluation) Claude Code, Cursor, Windsurf Experience leading AI strategy, GTM, or technical pre-sales initiatives Background in consulting or federal systems integrators (Booz Allen, Accenture, Deloitte, etc.) Show more Show less