Lang Chain Deployment AI Engineer

Nexaminds is looking for a LangChain Deployment AI Engineer to join our team and deliver production-grade generative and agentic AI solutions. The ideal candidate has strong hands-on experience with Python and/or JavaScript/TypeScript, LangChain, LangGraph, RAG, LLM applications, and cloud deployment, with proven experience taking AI solutions beyond proof-of-concept into reliable production environments.

This role focuses on translating approved AI architectures into secure, scalable, observable, and maintainable production systems, while working closely with AI architects, engineering teams, and customer stakeholders. The successful candidate will build and deploy LLM-powered applications, agentic workflows, RAG solutions, evaluation pipelines, and integrations, while ensuring strong quality, security, performance, and operational readiness.

Location: U.S.A (Remote)

Qualifications we are looking for:

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent practical experience.
• Strong professional software engineering experience with Python and/or JavaScript/TypeScript, including API development, asynchronous processing, testing, packaging, dependency management, and code review.
• Hands-on experience building production-grade AI agents using LangChain and LangGraph, including chains/runnables, tools, structured outputs, stateful workflows, streaming, persistence, and error handling.
• Experience developing and deploying LLM-powered applications using commercial model APIs such as OpenAI, Anthropic, Google, or cloud-hosted equivalents, and/or open-source models.
• Strong understanding of Retrieval-Augmented Generation (RAG), including document ingestion, chunking, embeddings, vector search, metadata filtering, reranking, grounding, and citation patterns.
• Experience working with at least one vector database or search platform, such as Pinecone, Weaviate, Milvus, pgvector/PostgreSQL, Elasticsearch/OpenSearch, Redis, Azure AI Search, or equivalent.
• Experience deploying production-grade AI applications using Docker and Kubernetes.
• Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Experience taking AI applications beyond POCs into production, including CI/CD, environment management, monitoring, troubleshooting, and operational support.
• Hands-on experience with LangSmith or comparable LLM tracing, evaluation, and observability tools, including the ability to investigate quality, latency, cost, and tool/retrieval failures.
• Familiarity with the Agent Development Life Cycle (ADLC) and production deployment of configurable AI agents.
• Working knowledge of application security, including IAM, secrets management, encryption, API security, auditability, privacy, and secure software development practices.
• Strong customer-facing communication, technical writing, and consulting skills, with the ability to explain technical tradeoffs to both engineering and business stakeholders.
• Ability to work effectively in ambiguous environments and collaborate closely with AI architects, engineering teams, platform teams, security, data, and product stakeholders.

Nice to have:

• Experience with LangChain enterprise products, including LangSmith, Fleet, or Deep Agents.
• Experience designing or implementing multi-agent systems, supervisor patterns, long-running workflows, durable execution, event-driven integrations, or human approval workflows.
• Experience with cloud AI platforms such as Amazon Bedrock, Azure AI Foundry/Azure OpenAI, or Google Vertex AI.
• Experience with managed Kubernetes platforms such as EKS, AKS, or GKE.
• Experience with Infrastructure as Code and delivery tools such as Terraform, Helm, GitHub Actions, GitLab CI, Jenkins, or Argo CD.
• Experience with observability platforms such as OpenTelemetry, Datadog, Grafana, Prometheus, CloudWatch, Azure Monitor, or Google Cloud Operations.
• Knowledge of LLM evaluation, model and prompt versioning, red teaming, adversarial testing, hallucination analysis, retrieval evaluation, and human feedback programs.
• Experience working in regulated or data-sensitive environments.
• Relevant certifications in cloud, Kubernetes, security, data engineering, or AI/ML.
• Willingness to travel to customer locations when required.

Job duties:

• 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.