Lang Chain Deployment AI Engineer

Nexaminds develops artificial intelligence solutions focused on transforming businesses, streamlining processes, and driving growth. The LangChain Deployment AI Engineer will build and deploy production-grade generative and agentic AI systems, including LLM applications, RAG solutions, evaluation pipelines, and integrations. The role emphasizes secure, scalable, observable, maintainable deployments and collaboration with architects, engineering teams, and customers.

Responsibilities:

• Translate approved LangChain and agentic AI architectures into maintainable, secure, scalable, and production-ready applications
• Build LLM-powered assistants, copilots, chatbots, document-processing solutions, decision-support tools, and automated workflows
• Develop deterministic and agentic workflows using LangGraph, including state management, routing, tool use, retries, checkpoints, memory, streaming, and failure recovery
• Design and implement RAG pipelines, including ingestion, chunking, embeddings, indexing, retrieval, reranking, grounding, citations, and access controls
• Integrate LLMs, embedding models, APIs, databases, search systems, vector stores, business applications, and custom tools through secure and maintainable interfaces
• Deploy AI applications using Docker, Kubernetes, serverless, or managed cloud runtimes, supporting environment promotion, rollback, autoscaling, resiliency, and disaster recovery
• Implement LangSmith tracing, evaluation, monitoring, dashboards, alerts, and feedback workflows to monitor model, tool, retrieval, latency, cost, and quality behavior
• Establish automated quality engineering and evaluation processes, including unit, integration, end-to-end, regression, adversarial, and load testing
• Build offline and online evaluation frameworks, golden datasets, quality thresholds, and release gates for prompts, models, retrieval systems, tools, and workflows
• Implement AI security and responsible AI practices, including least privilege, IAM, encryption, secrets management, audit logging, PII safeguards, prompt-injection defenses, output controls, and human escalation
• Optimize AI applications for response quality, latency, throughput, reliability, context utilization, caching, model selection, and token consumption
• Integrate AI solutions into CI/CD pipelines and establish reproducible deployment and release processes
• Create operational documentation, architecture updates, troubleshooting guides, runbooks, dashboards, alert thresholds, and incident procedures
• Partner with AI architects, Nexaminds delivery leaders, and customer stakeholders to communicate progress, risks, dependencies, and technical tradeoffs
• Participate in design and code reviews, mentor engineers, and support knowledge transfer to customer engineering and operations teams
• Monitor production systems, analyze feedback and failures, and continuously improve AI solution quality, reliability, performance, and cost