You will be taken to Excelra’s own careers page to apply. Job ID JOB882.
Job description
Location : Hybrid (Hyderabad/Bangalore) Experience : 6-8 Years Role Summary
The CMC Solutions Engineer will design and build the semantic and technical foundation for CMC data, with focus on two areas: developing the CMC ontology and knowledge model, and engineering automated, GxP-relevant stability data workflows.
Starting with stability monitoring data from CDMO-generated protocols and reports, this role will move CMC data away from siloed, manual processes (SharePoint, email, manual transcription into Excel) toward a structured, ontology-driven, auditable data layer that supports trending, alerting, regulatory reporting, and AI-assisted analysis.
The engineer will work closely with CMC subject matter experts (analytical development, stability, quality, and regulatory CMC), data architecture leads, and existing data catalog/data lake teams to deliver a proof of concept using CDMO data, then iterate toward a scalable, enterprise-reusable solution that extends beyond stability to manufacturing, specifications, reference standards, and document management.
Design, build, and maintain the CMC ontology, beginning with the stability domain (studies, protocols, storage conditions, pull points, lots/batches, tests, methods, specifications, results, and impurities) and extending to manufacturing, reference standards, and document management
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Translate CMC business concepts and SME knowledge into well-formed ontologies, controlled vocabulary
Align and explore applicability of established industry models where appropriate, such as the Allotrope Foundation Ontologies (AFO), Pistoia Alliance IDMP-O, and terminology consistent with ICH Q1 (stability) and ICH Q6 (specifications), rather than building from scratch
Map extracted and source-system data to ontology classes and properties, and maintain semantic mappings between CDMO-specific terminology and internal standards
Build and query knowledge graph representations of CMC data to enable cross-study, cross-lot, and cross-CDMO analysis
Establish ontology governance: versioning, change control, stewardship roles, and documentation that supports use in a regulated environment
Stability Workflows & Data Engineering
Map current-state and design future-state stability workflows end to end, from protocol setup and pull schedules through testing, results review, trending, out-of-trend (OOT) / out-of-specification (OOS) signaling, and reporting for regulatory submissions (e.g., CTD Module 3.2.P.8 / 3.2.S.7)
Design and build automated pipelines to ingest stability protocols, reports, and results from CDMO-generated PDFs and other sources
Develop parsing/extraction logic that handles varied PDF formats across CDMOs, including high-volume, structurally complex data
Standardize extracted data into an ontology-aligned, auditable structure, and use historical Excel data and linked PDFs as a validation set to measure and improve extraction accuracy
Enable downstream stability analytics, including trending, shelf-life estimation, and alerting (e.g., in JMP and Spotfire), in partnership with statisticians and stability scientists
Extend existing data catalog/data lake capabilities to support CMC use cases, ensuring the solution is reusable across the broader enterprise
Governance, Collaboration & Delivery
Build in governance, access control, traceability, data lineage, and audit trails consistent with GxP and data integrity (ALCOA+) expectations
Lay the groundwork for natural-language querying and AI-assisted analysis of CMC data, using the ontology and knowledge graph to ground results
Collaborate with CMC subject matter experts, data architecture leads, and IT to deliver an initial proof of concept within an agile, discovery-driven scope
Contribute to build-vs-buy-vs-integrate evaluations for stability management, LIMS, and semantic platform tooling
Document assumptions, data lineage, ontology design decisions, and workflow requirements to support the transition from bridge solution to long-term architecture
Required Qualifications
Bachelor’s degree in Computer Science, Data Engineering, Information Science, Life Sciences Informatics, Chemistry, or a related field; Master’s degree preferred
5+ years of experience in data engineering, knowledge engineering, or scientific informatics, including building data pipelines, ETL/ELT processes, or document/data extraction solutions
Hands-on experience designing ontologies, taxonomies, or semantic data models
Understanding of pharmaceutical stability programs and workflows (protocols, storage conditions, pull points, test methods, specifications, OOT/OOS) and the ICH Q1 guideline family
Hands-on experience with PDF/document parsing and extraction (e.g., OCR, layout-aware extraction, table extraction from unstructured or semi-structured PDFs)
Familiarity with regulated (GxP) data environments, including audit trail, traceability, data integrity (ALCOA+), and access-control requirements
Proficiency in Python (or similar) and SQL for data engineering and automation
Experience with relational, document, and/or graph databases, and integrating data from multiple source systems
Strong communication skills to translate between CMC scientists and technical teams, and to work directly with cross-functional stakeholders
Comfort working in an agile, discovery-driven environment with evolving scope
Preferred Qualifications
Direct experience in pharma/biotech CMC domains (stability studies, analytical chemistry, manufacturing data, specifications, or reference standards)
Familiarity with industry standards such as the Allotrope Foundation Ontologies / Allotrope Data Format, Pistoia Alliance CMC process ontology, or FAIR data principles
Experience with ontology editors and graph platforms (e.g., GraphDB, Stardog, Neo4j, Amazon Neptune)
Experience configuring or integrating stability management systems or LIMS stability modules, or platforms such as Benchling, SharePoint, JMP, or Spotfire
Familiarity with stability statistics and shelf-life estimation approaches (e.g., ICH Q1E regression analysis)
Exposure to AI/ML- or LLM-assisted document extraction, knowledge-graph-grounded AI, or natural-language querying
Experience working with CDMOs or other external data-generating partners
Familiarity with regulatory submission structures (eCTD / CTD Module 3) and computer system validation (CSV/CSA) practices
Background in proof-of-concept or pilot delivery within a build-vs-buy-vs-integrate decision process
Technical Skills & Tools
Languages : Python (primary), SQL, SPARQL; Cypher a plus
