J
Gen AI Engineer
Jobs via Dice
1 hour ago
Full-time
On-site
East New York, New York, United States
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.
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.