LANG CHAIN DEPLOYMENT AI ENGINEER ( LangGraph, LangSmith, Fleet, DeepAgents, Traces, Evaluations)

Role Mission

Key Responsibilities

• Customer deployment and delivery: Embed with customer project teams; clarify functional and nonfunctional requirements; convert architecture, user stories, and acceptance criteria into production-ready implementations; communicate progress, risks, tradeoffs, and dependencies clearly.
• LangChain application engineering: Build LLM-powered assistants, copilots, chatbots, document-processing solutions, decision-support tools, and automated workflows using LangChain APIs, SDKs, and integrations.
• Agentic workflow implementation: Develop deterministic and agentic workflows with LangGraph, including state management, routing, tool use, retries, checkpoints, human-in-the-loop controls, memory, streaming, and failure recovery.
• RAG and enterprise data integration: Design and implement ingestion, chunking, metadata, embedding, indexing, retrieval, reranking, grounding, citation, and access-control patterns across structured and unstructured customer data.
• Model and tool integration: Integrate hosted and open-source LLMs, embedding models, APIs, databases, search systems, vector stores, business applications, and custom tools using secure and maintainable interfaces.
• Production deployment: Package services in containers; deploy to Kubernetes, serverless, or managed cloud runtimes; define configuration and secrets handling; support environment promotion, rollback, autoscaling, resiliency, and disaster-recovery requirements.
• LangSmith observability and evaluation: Instrument traces, datasets, experiments, evaluators, feedback, alerts, and dashboards. Monitor model, tool, and retrieval behavior; latency; token usage; cost; error rates; and quality regressions.
• LangChain enterprise delivery: Apply applicable LangChain enterprise capabilities, including LangSmith and Fleet, to support governed development, deployment, management, and operational visibility in customer environments.
• Quality engineering: Create unit, integration, end-to-end, regression, adversarial, and load tests. Establish offline and online evaluation, golden datasets, quality thresholds, and release gates for prompts, models, retrieval, tools, and workflows.
• Security and responsible AI: Implement least privilege, identity and access controls, encryption, secrets management, audit logging, data minimization, PII safeguards, prompt-injection defenses, output controls, content safety, and human escalation appropriate to the use case.
• Performance and cost optimization: Improve response quality, latency, throughput, reliability, context utilization, caching, model selection, and token consumption while meeting agreed service-level objectives and budget constraints.
• Operational readiness: Create deployment documentation, architecture updates, troubleshooting guides, runbooks, dashboards, alert thresholds, on-call handoffs, and incident/post-incident procedures.
• Collaboration and knowledge transfer: Partner with LangChain architects, Nexaminds delivery leaders, and customer stakeholders; participate in design and code reviews; mentor engineers; and enable customer teams to operate and extend the delivered solution.

Typical Engagement Lifecycle

Required Qualifications

• 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.
• Familiarity with the ADLC (Agent Development Life Cycle) and deploying configurable production-grade agents that can be managed and monitored in cloud platforms or on-prem datacenters.
• Hands-on experience building agents with LangChain and LangGraph, including chains/runnables, tools, MCP servers, structured outputs, stateful workflows, streaming, persistence, and error handling.
• Experience delivering LLM applications using commercial model APIs such as OpenAI, Anthropic, Google, or cloud-hosted equivalents, and/or open-source models.
• Practical experience with embeddings, vector search, hybrid retrieval, metadata filtering, reranking, document ingestion, grounding, and citation patterns.
• Experience with at least one vector database or search platform, such as Pinecone, Weaviate, Milvus, pgvector/PostgreSQL, Elasticsearch/OpenSearch, Redis, Azure AI Search, or a cloud-native equivalent.
• Production experience with Docker and Kubernetes and with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.
• Experience deploying production-grade agents and LLM applications beyond proofs of concept (POCs), including CI/CD, environment management, monitoring, incident troubleshooting, and operational support.
• Hands-on experience with LangSmith or comparable LLM tracing, evaluation, and observability tooling; ability to investigate quality, latency, cost, and tool/retrieval failures from traces.
• Working knowledge of application security, IAM, secrets management, encryption, API security, auditability, privacy, and secure software-development practices.
• Strong customer-facing communication, technical writing, and consulting skills, including the ability to work through ambiguity and explain tradeoffs to engineers and business stakeholders.
• Ability and willingness to travel to customer sites when an engagement requires it.

Preferred Qualifications

• Experience with LangChain enterprise products and operating models, including LangSmith, Fleet and Deep Agents.
• Experience designing or implementing multi-agent systems, supervisor patterns, long-running workflows, durable execution, event-driven integration, and human approval steps.
• Experience with cloud AI platforms such as Amazon Bedrock, Azure AI Foundry/Azure OpenAI, Google Vertex AI, or managed Kubernetes platforms such as EKS, AKS, or GKE.
• Experience with infrastructure as code and delivery tooling such as Terraform, Helm, GitHub Actions, GitLab CI, Jenkins, Argo CD, or equivalent platforms.
• Experience with OpenTelemetry and observability platforms such as Datadog, Grafana, Prometheus, CloudWatch, Azure Monitor, or Google Cloud Operations.
• Knowledge of LLM evaluation methods, model/prompt versioning, red teaming, adversarial testing, hallucination analysis, retrieval evaluation, and human feedback programs.
• Experience in regulated or data-sensitive environments such as healthcare, financial services, insurance, manufacturing, or the public sector.
• Relevant cloud, Kubernetes, security, data engineering, or AI/ML certifications.

Technical Competencies