Lang Chain Deployment AI Engineer

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.