Role: MarkLogic Solutions Architect

Location: Pune

ACS-I India is looking for an experienced MarkLogic architect to design and own the technical architecture of MarkLogic solutions for Content Engine, the big data function that maintains and grows the data-lake repository and architecture. The role combines platform architecture on MarkLogic with solution architecture from requirements to production across connected systems. This is a working role. The architect designs, prototypes, reviews, and troubleshoots alongside the team, and answers for the design quality, performance, security, and operability of what the team releases. The role also covers AI and generative AI, including how the team prepares CAS content for retrieval and how AI tools reach it safely.

The architect has technical authority over the team’s designs and no line-management duties. The MarkLogic team lead manages the team’s people, capacity, and sprint commitments. The architect decides data models, indexing and search design, integration patterns, cluster and cloud topology, release track, and AI retrieval patterns, and works with the team lead on delivery.

Job responsibilities

Architecture and solution design

- Own the end-to-end architecture of MarkLogic solutions that deliver CAS content, products, and solutions, including XML and JSON data models, indexing strategy, search design, content processing, and service interfaces.
- Turn business requirements into solution designs with clear scope, constraints, and acceptance criteria, and explain each design to product owners, client teams, and engineers.
- For each significant decision, present at least two workable options with their pros, cons, cost, and risk; recommend one; and record the decision, its assumptions, and the evidence that would change it.
- Define non-functional requirements for each solution as measurable targets covering performance, scalability, availability, security, data governance, recoverability, operability, and cost. Test the targets before release and monitor them in production.
- Design and govern integrations between upstream content sources and downstream consumers, including REST services, bulk and batch processing with CoRB and Flux, event streaming, and index synchronization. Own the interface contracts, error handling, and reconciliation.
- Run architecture and design reviews, and confirm that solutions follow enterprise standards for security, data governance, and compliance.

End-to-end delivery and technical leadership

- Stay accountable for each solution from requirement to production support. Work with product owners to groom the backlog, estimate effort, and split large items into deliverable increments.
- Act as the technical authority on architecture for stakeholders and client teams, and report architecture risks and dependencies early.
- Review code, queries, and configuration. Pair with engineers on the hardest problems. Troubleshoot production incidents and lead root-cause analysis through to a permanent fix.
- Mentor and coach engineers through design walk-throughs, code reviews, and pair programming, and take part in technical hiring.
- Set coding standards and quality gates, and drive automated builds, automated testing,



and continuous delivery.

Platform, cloud, and operations

- Own the target-state architecture and the plan for moving MarkLogic workloads from on-premises infrastructure to AWS, starting with lift-and-shift and then adopting managed services where they fit. Compare self-managed AWS deployment, Kubernetes, and Progress Data Cloud, and recommend one.
- Set the release and upgrade strategy, including the choice between the long-term support and innovation release tracks, and plan upgrades and patching with minimal disruption to consumers.
- Set standards for capacity, scaling, replication, backup and restore, disaster recovery, monitoring, and alerting, and maintain the runbooks that support them.
- Lead query and index tuning at scale with XQuery, CTS, and Optic, using MarkLogic profiling and query-plan tools to find root causes and set performance baselines.

AI and generative AI

- Design AI-ready content and retrieval on MarkLogic, including the embedding and chunking approach, hybrid search that combines full-text, semantic, and vector retrieval, relevance tuning, and grounding of generated answers in CAS content.
- Define how AI assistants and agents reach MarkLogic content through the Model Context Protocol (MCP) and other interfaces, with access control, an audit trail, and content filtering applied before data reaches a model.
- Evaluate MarkLogic releases and generative AI tools, and lead proofs of concept with written success criteria for retrieval accuracy, latency, and cost before the team adopts a change.
- Set rules for the team’s use of AI coding and testing assistants on XQuery, JavaScript, and test code, including human review and data-handling limits.

Technologies and tools

MarkLogic Server 10 or later, XQuery, XSLT, XPath, Optic API, CTS queries, Template-Driven Extraction (TDE), REST APIs, CoRB, Flux, Java, Linux, Bash, Git, Jenkins, AWS (EC2, EBS, S3, VPC, IAM, and CloudWatch), CI/CD, Jira, and Confluence.

Job requirements

- Education: Four-year degree in computer science or engineering, or equivalent experience.
- Experience: At least 12 years in application development, solution delivery, and operations, including at least seven years of hands-on MarkLogic work and at least three years as an architect or technical design authority, formally or informally, for a production data platform.
- MarkLogic: Advanced knowledge of MarkLogic Server 10 or later, including XQuery, XSLT, XPath, the CTS and Optic search APIs, indexing, Template-Driven Extraction, security, query tuning, and cluster administration, plus working knowledge of MarkLogic 12 features such as native vector search and virtual views.
- Data modeling: Experience designing XML and JSON document models for large content repositories, with working knowledge of semantics (RDF triples and SPARQL).
- Solution architecture:



Experience architecting solutions for large content or data platforms, from requirements to design documents that engineers can build from.
- Decision-making: Ability to compare alternative solutions on pros, cons, cost, and risk, recommend one with clear reasoning, record the decision and the evidence that would change it, and revisit it when that evidence appears.
- Non-functional requirements: Strong experience defining measurable targets for a production platform and verifying them through load, failure, and security testing.
- Integration: Experience designing integrations across multiple systems, including REST services, batch and bulk processing, and event streaming, with defined interface contracts, error handling, and reconciliation.
- End-to-end delivery: Experience owning a product or platform through design, build, release, and production support.
- Stakeholders: Ability to work directly with product owners, client teams, and non-technical stakeholders, translate business requirements into technical solutions, and explain trade-offs in writing and in meetings.
- Java, Linux, and scripting: Solid experience with Java, Linux, application servers, and backend processing, and mid-level to advanced Bash or Kshell.
- AWS: Working knowledge of EC2, EBS, S3, VPC, IAM, and CloudWatch, and experience designing or running a production data platform on AWS.
- Generative AI: Working knowledge of retrieval patterns (embeddings, chunking, hybrid search, and retrieval-augmented generation) and of access control and data-privacy limits for AI features, shown through a design, a proof of concept, or a production system. No minimum number of years applies.
- CI/CD and testing: Hands-on experience with Git and Jenkins pipelines, including automated deployment of MarkLogic code and configuration, and with test-driven development.
- Production operations: Experience with monitoring, incident response, backup and recovery, and capacity planning for production data platforms.
- Technical leadership: Ability to set direction without line authority, run design reviews, give clear code-review feedback, estimate work, mentor engineers at junior and senior levels, and pair with others when it helps.
- Ownership: Accountability for design quality, delivery, and production stability; early escalation of risks; and continued learning in MarkLogic and AI.

Nice to have

- MarkLogic Data Hub Framework for ingestion, harmonization, curation, and metadata enrichment.
- Server-Side JavaScript, MLCP, and the Data Movement SDK.
- Exposure to GraphQL.
- Bi-temporal and geospatial features in MarkLogic.
- Running MarkLogic on AWS with the Managed Cluster feature and CloudFormation templates, or on Kubernetes.
- AWS certification, such as Solutions Architect Associate or Professional.
- Apache Kafka, Apache Spark, Scala, Hadoop, and Solr/Lucene.
- Docker, Kubernetes, Terraform, CloudFormation, or Ansible, and GitLab CI alongside Jenkins.
- LangChain or langchain4j, LLM evaluation, and hands-on work with MCP servers.
- Scrum or SAFe delivery experience in a distributed global team across time zones.
- Familiarity with large content repositories, XML publishing standards, and scientific or patent content.

📌 MarkLogic Solutions Architect (Pune)
🏢 ACS International India (ACSII
📍 Pune