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# Gen AI Engineer

- **Company:** AIT Global, Inc.
- **Location:** Evanston, IL, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Program Optimization, DevOps, Python (Programming Language), Language Modeling, Large Language Models, Multi-Agent Systems, Build Tools, GPT
- **Published:** August 21, 2026
- **Apply:** https://www.dice.com/job-detail/0cbb48e6-9a81-40d9-8cc8-8032fb6cd6a8

## About the Role

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., * 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.

## 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.


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