Senior Associate

Job description

**Position Summary** Design and develop AI/GenAI solutions for Commercial Pharma data use cases, including enterprise knowledge search, insight generation, AI agents, natural language analytics and productivity accelerators. The role works from Vizag with data, platform, governance and business teams to build secure and scalable AI capabilities. **Business / Program Context** The role is part of a Vizag-based delivery capability supporting Commercial Pharma datasets across global markets. The work may include platform operations, data engineering, data quality, reporting, AI enablement, release governance and stakeholder support for enterprise commercial data products. The role is not limited to Japan-specific datasets; Japan market familiarity may be useful but is not a mandatory baseline requirement for most positions. **Job Responsibilities** - Develop GenAI applications, AI agents and RAG-based solutions for commercial data use cases. - Build prompt engineering patterns, retrieval pipelines, LLM integration and model evaluation workflows. - Use Databricks AI/Mosaic AI capabilities where applicable for model serving, vector search and experimentation. - Integrate AI solutions with curated commercial datasets, knowledge bases, metadata and documentation repositories. - Implement guardrails for security, privacy, accuracy, hallucination reduction and responsible AI usage. - Partner with AI/Data Engineers, Platform Engineers and business stakeholders to industrialize AI solutions. - Document solution design, evaluation metrics, prompts, data sources and governance controls. - Work as part of a Vizag-based delivery team supporting Commercial Pharma datasets and global commercial data stakeholders. - Collaborate with business, technology, governance, quality and offshore/onshore delivery teams across time zones. - Follow Agile delivery, SDLC, documentation, release, validation, access-control and data-governance standards. - Contribute to knowledge management, reusable assets, SOPs, runbooks and operational excellence initiatives. - Maintain strong communication discipline, including status updates, risk identification, issue escalation and timely stakeholder follow-through. **Required Work Experience** - 48 years of relevant experience aligned to the role and seniority level. - Prior experience in pharmaceutical, life sciences, healthcare analytics, commercial data platforms or enterprise data programs. - Experience working in a global delivery model with structured governance, documentation, SLAs and stakeholder communication. - Ability to work from Vizag and collaborate effectively with India, global and client teams. **Technical / Functional Skills** - Python - LLM integration - RAG and Vector Databases - Databricks AI / Mosaic AI - Prompt engineering - LangChain/LlamaIndex or equivalent - Model evaluation - AI governance and responsible AI **Commercial Pharma Dataset Exposure** - Pharmaceutical commercial data platforms - Sales, prescription, claims, CRM and omnichannel datasets - Customer master, HCP/HCO, product master, territory and alignment datasets - Commercial KPIs, field force effectiveness, market share and promotional effectiveness - Data quality, reconciliation, lineage, metadata and controlled release processes **Desirable Skills** - Experience supporting global pharma companies or commercial data programs. - Exposure to Eisai-like commercial operating models, field reporting, sales operations and analytics use cases. - Japan market dataset exposure is preferred but not mandatory unless the assignment requires direct Japan business engagement. - Strong written and verbal communication skills; ability to explain data, risks, defects and business impact clearly. - Certifications in Databricks, Informatica IDMC, Azure/AWS, Scrum, ITIL, Tableau/Power BI or AI/ML are preferred depending on role. **Success Measures / KPIs** - Secure, useful and governed AI solutions with measurable productivity, search, analysis or decision-support value. **Indicative 30 / 60 / 90 Day Expectations** 1. First 30 days: complete onboarding, understand commercial data landscape, delivery processes, documentation standards and role-specific tools. 2. First 60 days: independently contribute to assigned workstreams, support issue resolution, produce required artifacts and participate in governance forums. 3. First 90 days: demonstrate measurable ownership, improve delivery quality, identify improvement opportunities and support stable operations or project outcomes.