Note: The job is a remote job and is open to candidates in USA. Dice is seeking a Gen AI Engineer for a remote role based in Evanston, Illinois. The engineer will design and implement agentic workflows, optimize LLM inference pipelines, manage retrieval systems, and develop evaluation processes for generative AI applications.
Responsibilities
- 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)
- 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
- 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
Skills
- Agentic Workflows (Strong)
- Communication & collaboration (Strong)
- Lang chain
- Python (Strong)
- LLM Foundations
- 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)
- 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
- 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
- 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
- Experience running and tuning LLM inference workloads
- Familiarity with vLLM or similar inference engines
- Experience with LLM evaluation frameworks and metric-driven iteration
- DevOps - GenAI, Transformer-based models and seq-to-seq paradigms
- Pharma industry/domain experience is preferred
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