AI Engineers - USA

Indicium AI•New York, NY

10d•Remote

About The Position

We're seeking an experienced AI Engineer to design, build, and deploy production-grade AI systems powered by large language models. This role sits at the intersection of software engineering and AI implementation, focusing on building reliable, scalable applications rather than model training or research. You'll work with cutting-edge LLM technologies, building advanced AI systems that solve complex real-world problems through multi-agent orchestration, intelligent tool integration, and robust production workflows. You'll be crafting the orchestration layer that makes these systems production-ready—handling failure modes, optimizing agent collaboration, and ensuring consistent, reliable outputs at scale. You’ll combine strong software engineering fundamentals with deep practical knowledge of LLM capabilities, limitations, and best practices for building non-deterministic systems that users can trust.

Requirements

Hands-on experience building applications with LLM APIs and deep understanding of their capabilities, limitations, and failure modes
Practical implementation of RAG architectures, vector databases, knowledge graphs and prompt engineering
Experience building multi-step LLM workflows and agentic systems using frameworks (e.g. SDK, Strands, Claude Agents SDK, LangGraph, etc.) or custom implementations where needed
Strong Python (or other modern programming language) proficiency with production API/service development experience and cloud platform knowledge (AWS, GCP, Azure)
Understanding of distributed systems, CI/CD, testing frameworks, and deployment pipelines
Solid foundations and understanding of production-grade, cloud-native platform and infrastructure requirements, design, and implementation.
Strong data manipulation skills (pandas, SQL) and understanding of evaluation strategies for LLM-based systems
Ability to work with ambiguity and optimise non-deterministic systems through a process of experimentation and evaluation while balancing latency/cost/quality tradeoffs

Nice To Haves

Experience with AI-assisted coding using tools like Claude Code, OpenAI Codex, Github Copilot
Experience with fine-tuning LLMs for domain-specific applications and knowledge of when fine-tuning is preferable to prompt engineering or RAG
Experience with real-time streaming, multimodal models, or search technologies like Elasticsearch
Familiarity with model observability tools (LangSmith, Weights & Biases) and cost optimization strategies
Experience in specialized verticals (financial services, energy, healthcare, legal, retail) with understanding of compliance, security, and responsible AI practices
Experience with setting up tool calling agents, handoffs, and guardrails

Responsibilities

Design and implement production AI systems integrating LLMs, RAG pipelines, vector databases, and agentic frameworks.
Create evaluation frameworks to measure and monitor system performance, accuracy, and reliability
Build and maintain production-grade AI applications with clean code, appropriate error handling, APIs, and data pipelines
Experience implementing, maintaining and evaluating retrieval systems (vector/graph databases, ingestion pipelines, chunking strategies, retrieval techniques such as HyDE)
Implement feedback loops and observability to continuously improve system performance
Craft effective prompts and optimize for latency, cost, and quality across different model providers and configurations