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Gen AI Engineer

AIT Global, Inc.Evanston, IL🇺🇸United StatesPosted 20 Aug 2026

Why This Role Stands Out

This remote Gen AI Engineer role at AIT Global offers a fantastic opportunity to shape the future of AI by designing and implementing complex agentic workflows, with significant potential for skill development in cutting-edge LLM technologies. You'll thrive here if you have strong experience with agentic workflows, LangChain, and Python, and enjoy tackling challenging problems in a collaborative, innovative environment. Apply to join a leading company and contribute to groundbreaking AI advancements.

Quick Overview

Seniority
Mid Senior
Work mode
Remote
Location
Evanston, IL, United States
Posted
Yesterday
LLMPython

Job Description

Job Title: Gen AI Engineer
Location: Remote - Evanston, IL
Must have skills:
Agentic Workflows (Strong), Communication & collaboration (Strong), Lang chain, Python (Strong), LLM Foundations.
Good to have skills:
  • DevOps - GenAI, Transformer-based models and seq-to-seq paradigms.
  • Pharma industry/domain experience is preferred.
Job Description:
  • Design and implement stateful multi-agent workflows using LangGraph (checkpointers, retries, subgraphs, tool calling).
  • Define Agent-to-Agent (A2A) interaction patterns for decomposition, verification, and self-correction.
  • Build tool-using agents with structured outputs, schema enforcement, and deterministic execution paths.
  • Handle agent failure modes such as hallucinations, tool misuse, and partial execution.
  • Select and tune vector stores (FAISS, Milvus, Pinecone, Weaviate). Inference & Model Optimization
  • Operate and optimize LLM inference pipelines with focus on latency, throughput, and cost.
  • Work with vLLM (continuous batching, memory efficiency).
  • Make informed trade-offs between model size, context length, and output quality.
  • Apply quantization and other inference-time optimizations where required. Evaluation & Iteration
  • Design and run LLM evaluation workflows using tools such as LangSmith, Ragas, TruLens, or equivalent.
  • Define acceptance metrics for Grounded Ness, Context relevance, Answer quality.
  • Use evaluation results to iterate on prompts, retrieval strategies, and agent design.
  • Ability to reason about: Attention mechanisms and scaling, Decoder-only vs encoder decoder architectures o Prompting vs retrieval vs fine-tuning trade-offs.
  • Hands-on experience solving non-trivial GenAI use cases. Agentic & RAG Expertise
  • Proven experience building agentic workflows with LangGraph.
  • Strong understanding of tool calling, structured outputs, and schema contracts.
  • Deep experience with RAG systems, including retrieval evaluation and optimization.
  • Experience with vector databases and embedding strategies. Inference & Evaluation
  • Experience running and tuning LLM inference workloads.
  • Familiarity with vLLM or similar inference engines.
  • Experience with LLM evaluation frameworks and metric-driven iteration.

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