{"id":1191650,"url":"https://alion.io/job/nexaminds-langchain-deployment-ai-engineer-langgraph-langsmith-fleet-deepagents-traces-evaluations","title":"Langchain Deployment AI Engineer ( LangGraph, LangSmith, Fleet, DeepAgents, Traces, Evaluations)","company":{"id":2681241,"name":"Nexaminds","domain":"nexaminds.ai","url":"https://alion.io/company/nexaminds","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"IAM","optional":false},{"name":"JavaScript","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Milvus","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Reranking","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false},{"name":"Weaviate","optional":false},{"name":"Amazon CloudWatch","optional":true},{"name":"Amazon EKS","optional":true},{"name":"ArgoCD","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Azure AKS","optional":true},{"name":"Context Engineering","optional":true},{"name":"Datadog","optional":true},{"name":"GitHub Actions","optional":true},{"name":"GitLab CI","optional":true},{"name":"Google GKE","optional":true},{"name":"Grafana","optional":true},{"name":"GraphQL","optional":true},{"name":"Hallucination","optional":true},{"name":"Helm","optional":true},{"name":"Hybrid Search","optional":true},{"name":"Jenkins","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Platform Engineering","optional":true},{"name":"Prometheus","optional":true},{"name":"Red Teaming","optional":true},{"name":"Rest API","optional":true},{"name":"SQL","optional":true},{"name":"Terraform","optional":true},{"name":"Vertex AI","optional":true}],"status":"closed","first_seen_at":"2026-08-21T09:04:13Z","employer_posted_date":"2026-08-26","last_verified_at":"2026-09-28T10:02:55Z","board_verified":false,"closed_at":"2026-09-28T10:02:55Z","days_open":38,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":38},"description":"Unlock Your Future with Nexaminds!\nAt Nexaminds, we're on a mission to redefine industries with AI. We're passionate about the limitless potential of artificial intelligence to transform businesses, streamline processes, and drive growth.\nJoin us on our visionary journey. We're leading the way in AI solutions, and we're committed to innovation, collaboration, and ethical practices. Become a part of our team and shape the future powered by intelligent machines. If you're driven by ambition, success, fun, and learning, Nexaminds is where you belong.\nAbout Nexaminds\nNexaminds is an AI and technology consulting company that helps organizations design, build, deploy, and operate production-grade generative and agentic AI solutions. We combine architecture, hands-on engineering, cloud delivery, and responsible AI practices to move customer initiatives from strategy and prototype to secure, production grade scalable business systems.\nPosition Summary\nNexaminds is seeking a hands-on LangChain Deployment AI Engineer to join our full-time consulting team and deliver customer-facing generative and agentic AI solutions. This role is designed for an engineer who can translate an approved agentic AI architecture into reliable production grade agents, work effectively alongside LangChain architects, and embed with customer engineering, platform, security, data, and product teams.\nThe engineer will build and deploy applications using LangChain and LangGraph; implement retrieval-augmented generation (RAG), agentic workflows, tool integrations, and evaluation pipelines; and own the operational details required to run those systems in production while monitoring agents using LangSmith. The successful candidate goes beyond demonstrations and notebooks. They can deliver tested services, secure integrations, automated releases, observability, cost controls, runbooks, and measurable production outcomes.\nRole Mission\nConvert LangChain solution architectures into maintainable, secure, observable, and scalable production deployments while accelerating customer adoption and transferring operational knowledge to customer teams.\nKey Responsibilities\nCustomer 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.\nLangChain application engineering: Build LLM-powered assistants, copilots, chatbots, document-processing solutions, decision-support tools, and automated workflows using LangChain APIs, SDKs, and integrations.\nAgentic 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.\nRAG 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.\nModel 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.\nProduction 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.\nLangSmith 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.\nLangChain enterprise delivery: Apply applicable LangChain enterprise capabilities, including LangSmith and Fleet, to support governed development, deployment, management, and operational visibility in customer environments.\nQuality 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.\nSecurity 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.\nPerformance 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.\nOperational readiness: Create deployment documentation, architecture updates, troubleshooting guides, runbooks, dashboards, alert thresholds, on-call handoffs, and incident/post-incident procedures.\nCollaboration 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.\nTypical Engagement Lifecycle\nDiscover and validate: Customer use case, environment and tech stack assessment, review the agentic AI architecture, use cases, data boundaries, security constraints, success metrics, dependencies, and deployment environment.\nBuild and integrate: Implement workflows, RAG, tools, APIs, persistence, identity, guardrails, and user-facing or service interfaces.\nEvaluate and harden: Establish datasets and evaluators; test quality, safety, concurrency, failure modes, recovery, and cost.\nDeploy and operate: Automate infrastructure and releases; configure observability, alerts, dashboards, runbooks, and service ownership.\n Transfer and improve: Train customer teams, resolve early-life issues, analyze production feedback, and prioritize measurable improvements.\nRequired Qualifications\nBachelor’s degree in computer science, engineering, information systems, or a related discipline, or equivalent practical experience.\nStrong professional software-engineering experience with Python and/or JavaScript/TypeScript, including API development, asynchronous processing, testing, packaging, dependency management, and code review.\nFamiliarity 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.\nHands-on experience building agents with LangChain and LangGraph, including chains/runnables, tools, MCP servers, structured outputs, stateful workflows, streaming, persistence, and error handling.\nExperience delivering LLM applications using commercial model APIs such as OpenAI, Anthropic, Google, or cloud-hosted equivalents, and/or open-source models.\nPractical experience with embeddings, vector search, hybrid retrieval, metadata filtering, reranking, document ingestion, grounding, and citation patterns.\nExperience 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.\nProduction experience with Docker and Kubernetes and with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.\nExperience deploying production-grade agents and LLM applications beyond proofs of concept (POCs), including CI/CD, environment management, monitoring, incident troubleshooting, and operational support.\nHands-on experience with LangSmith or comparable LLM tracing, evaluation, and observability tooling; ability to investigate quality, latency, cost, and tool/retrieval failures from traces.\nWorking knowledge of application security, IAM, secrets management, encryption, API security, auditability, privacy, and secure software-development practices.\nStrong customer-facing communication, technical writing, and consulting skills, including the ability to work through ambiguity and explain tradeoffs to engineers and business stakeholders.\nAbility and willingness to travel to customer sites when an engagement requires it.\nPreferred Qualifications\nExperience with LangChain enterprise products and operating models, including LangSmith, Fleet and Deep Agents.\nExperience designing or implementing multi-agent systems, supervisor patterns, long-running workflows, durable execution, event-driven integration, and human approval steps.\nExperience 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.\nExperience with infrastructure as code and delivery tooling such as Terraform, Helm, GitHub Actions, GitLab CI, Jenkins, Argo CD, or equivalent platforms.\nExperience with OpenTelemetry and observability platforms such as Datadog, Grafana, Prometheus, CloudWatch, Azure Monitor, or Google Cloud Operations.\nKnowledge of LLM evaluation methods, model/prompt versioning, red teaming, adversarial testing, hallucination analysis, retrieval evaluation, and human feedback programs.\nExperience in regulated or data-sensitive environments such as healthcare, financial services, insurance, manufacturing, or the public sector.\nRelevant cloud, Kubernetes, security, data engineering, or AI/ML certifications.\nTechnical Competencies\nCore frameworks: LangChain, LangGraph; LangSmith for tracing, evaluation, monitoring, and feedback workflows\nLanguages: Python; JavaScript and/or TypeScript; SQL; shell scripting\nLLM engineering: Prompt and context engineering, structured output, tool/function calling, model routing, streaming, memory, caching, guardrails, evaluation\nData and retrieval: Document parsing, chunking, embeddings, vector/hybrid search, reranking, metadata and ACL filtering, relational and NoSQL data\nPlatform engineering: REST/GraphQL APIs, event queues, Docker, Kubernetes, CI/CD, infrastructure as code, secrets and configuration management\nOperations: Distributed tracing, metrics, logs, dashboards, alerting, SLOs, load testing, incident response, cost management\nWhat you can expect from us\nHere at Nexaminds, we're not your typical workplace. We're all about creating a friendly and trusting environment where you can thrive. Why does this matter? Well, trust and openness lead to better quality, innovation, commitment to getting the job done, efficiency, and cost-effectiveness.\nStock options \nRemote work options \nFlexible working hours \nBenefits above the law\nBut it's not just about the work; it's about the people too. You'll be collaborating with some seriously awesome IT pros.\nYou'll have access to mentorship and tons of opportunities to learn and level up.\nReady to embark on this journey with us? 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