Top Skills' Details- Masters degree in Computer Science, Machine Learning, Data Science, Engineering, or related discipline.- 10+ years of software engineering, machine learning engineering, or applied AI experience.- 3+ years building production AI, LLM, or Generative AI solutions.- Strong Python development experience.- Experience developing production-grade Retrieval-Augmented Generation (RAG) systems.- Experience building or supporting Agentic AI, AI orchestration, or multi-agent workflows.DescriptionAbout the RoleOur client is seeking a Senior Machine Learning Engineer II to help build and scale advanced Agentic AI and Multi-Agent Systems that transform how professionals interact with information.This role goes beyond traditional machine learning engineering. The ideal candidate will have experience designing intelligent AI systems capable of reasoning, planning, retrieval, tool utilization, workflow orchestration, and autonomous task execution across large-scale knowledge environments. You will work closely with Applied Scientists, ML Engineers, Architects, Product Leaders, and Software Engineers to develop production-grade AI systems that leverage Large Language Models (LLMs), RAG architectures, vector search, agent frameworks, and emerging reasoning technologies.This position is ideal for engineers who have moved beyond basic prompt engineering and have experience building sophisticated AI applications capable of solving complex, multi-step business problems.Key ResponsibilitiesAgentic AI DevelopmentDesign, build, and deploy production-scale multi-agent AI systems.Develop agent workflows capable of planning, reasoning, retrieval, tool utilization, validation, and task execution.Architect agent ecosystems utilizing specialized agent roles such as:PlannerResearcherCriticVerifierWriterOrchestratorImplement shared memory, state management, context preservation, and agent communication frameworks.Develop guardrails and validation systems to improve reliability and reduce hallucinations.Retrieval-Augmented Generation (RAG)Design and optimize enterprise-scale RAG architectures.Develop advanced retrieval strategies leveraging:Vector databasesSemantic searchKnowledge graphsMetadata filteringHybrid retrieval approachesImprove grounding, citation accuracy, retrieval quality, and relevance.Optimize chunking strategies, embedding pipelines, and context management.Machine Learning EngineeringBuild scalable AI and machine learning services deployed into production environments.Develop model evaluation frameworks for both traditional machine learning and LLM-based systems.Create automated testing pipelines for prompts, retrieval systems, agent workflows, and AI outputs.Fine-tune, evaluate, and optimize AI systems for performance, latency, quality, and cost.AI Evaluation & ObservabilityDefine and measure success metrics for agentic AI systems, including:Task completion ratesAccuracyHallucination ratesRetrieval effectivenessCost efficiencyUser satisfactionLatencyImplement monitoring, observability, and evaluation frameworks for LLM applications.Develop processes for continuous improvement and model governance.Research & InnovationEvaluate emerging AI technologies and frameworks.Investigate advances in:Multi-Agent SystemsAgentic AIReasoning ModelsLLM OrchestrationKnowledge RetrievalAutonomous AI WorkflowsContribute to architecture standards and AI platform strategy.Participate in proof-of-concepts and innovation initiatives.Required QualificationsBachelor's degree in Computer Science, Machine Learning, Data Science, Engineering, or related discipline.6+ years of software engineering, machine learning engineering, or applied AI experience.3+ years building production AI, LLM, or Generative AI solutions.Strong Python development experience.Experience developing production-grade Retrieval-Augmented Generation (RAG) systems.Experience building or supporting Agentic AI, AI orchestration, or multi-agent workflows.Strong understanding of:Large Language Models (LLMs)Natural Language Processing (NLP)Information RetrievalSemantic SearchEmbeddingsPrompt EngineeringModel EvaluationExperience working with vector databases and retrieval platforms.Strong software engineering fundamentals including testing, CI/CD, observability, and scalable system design.Preferred QualificationsExperience with agent frameworks such as:LangGraphAutoGenCrewAISemantic KernelOpenAI Agents SDKLlamaIndex WorkflowsExperience implementing:Multi-agent communication patternsShared memory architecturesTool-calling agentsAutonomous workflowsHuman-in-the-loop systemsExperience with cloud platforms including AWS, Azure, or GCP.Familiarity with:KubernetesDockerMLOpsLLMOpsAI observability platformsKnowledge graph or enterprise search experience.Experience building AI systems in highly regulated or knowledge-intensive domains.SkillsPython, Data science, Machine learning, Sql, Algorithm, Aws, agentic ai, mcp, Data, Predictive modelling, Artificial intelligenceTop Skills DetailsPython,Data science,Machine learning,Sql,Algorithm,Aws,agentic ai,mcpAdditional Skills & QualificationsN/AExperience LevelExpert LevelJob Type & LocationThis is a Contract to Hire position based out of Raleigh, NC.Pay and BenefitsThe pay range for this position is $75.00 - $85.00/hr. Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits available for this temporary role may include the following:
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