This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr Generative AI Engineer (Agentic AI & RAG) based in India.
This is a hands-on engineering role focused on building production-grade generative AI systems for complex healthcare and patient-services use cases.
You’ll turn enterprise AI strategy into working agents, RAG pipelines, integrations, and reusable engineering patterns.
The role combines advanced Python development with LLM application development, agent orchestration, retrieval architecture, and enterprise integrations.
You’ll work across platforms including GCP, Vertex AI, Claude, Gemini, Salesforce, MuleSoft, and Java-based systems.
Your work will help automate critical workflows such as intake, claims, adverse-event detection, workload management, and quality processes.
You’ll own AI solutions throughout their lifecycle, from development and evaluation to deployment, monitoring, optimization, and retirement.
This is an opportunity to build meaningful AI technology in a regulated healthcare environment while establishing standards that other engineering teams can reuse.
Accountabilities:
- Design, develop, and productionize AI agents using Vertex AI Agent Builder, Claude APIs, Gemini Enterprise, and related enterprise AI technologies.
- Build AI-powered workflows for priority patient-services use cases, including intake automation, missing-information workflows, adverse-event detection, workload queue intelligence, QNCR automation, and chat-with-claims capabilities.
- Develop and maintain production-grade RAG pipelines, including vector stores, embeddings, retrieval strategies, and data-access workflows.
- Implement MCP server integrations and agent tools/function definitions that allow AI agents to interact safely with enterprise systems.
- Integrate AI capabilities into Salesforce Health Cloud, including Apex and LWC, as well as MuleSoft/DataWeave and Java-based SDLC environments.
- Create reusable prompt templates, agent architectures, implementation patterns, and engineering components that can be adopted across development teams.
- Contribute to a shared, quality-reviewed, version-controlled prompt and agent pattern library.
- Own the complete agent lifecycle, including development, deployment, testing, evaluation, monitoring, optimization, and retirement.
- Establish evaluation harnesses and quality standards to validate agent accuracy, reliability, safety, and performance before production deployment.
- Review AI-generated code produced by Salesforce, MuleSoft, and Java developers to ensure quality, maintainability, and compliance with engineering standards.
- Monitor production agents for accuracy, latency, cost, performance, and model or workflow drift, taking corrective action when required.
- Collaborate with technical leadership and distributed engineering teams to implement enterprise AI strategy and support a follow-the-sun delivery model.
- Deliver first-year priorities including an Intake Automation MVP and at least two additional AI workflow MVPs.
- Drive adoption of reusable agent patterns, prompt libraries, evaluation frameworks, and monitoring practices across the broader engineering organization.
Requirements:
- 5+ years of professional software engineering experience with demonstrated success building and supporting production systems.
- Advanced Python development skills and working proficiency with JavaScript and/or TypeScript.
- Direct experience working with a cloud AI platform, with strong preference for hands-on experience with Vertex AI and its SDK.
- Demonstrated experience building LLM applications and applying prompt-engineering techniques with Claude, Gemini, OpenAI, or comparable foundation models.
- Practical experience designing and implementing RAG architectures, vector databases, embeddings, retrieval workflows, and associated evaluation approaches.
- Experience with agent orchestration frameworks such as CrewAI, LangChain, LangGraph, or Vertex AI Agent Builder.
- Experience integrating AI or software applications through REST and/or GraphQL APIs.
- Salesforce and/or MuleSoft integration experience is strongly valued, particularly with Apex, LWC, or DataWeave.
- Experience with MCP (Model Context Protocol) servers and agent tooling standards is a strong advantage.
- Background in healthcare or life-sciences technology, with awareness of regulated data environments and PHI/HIPAA considerations, is preferred.
- Experience with Salesforce development or Java development is an additional advantage.
- Familiarity with BigQuery, PostgreSQL, vector stores, or comparable data platforms.
- Strong understanding of the software development lifecycle and the ability to bring AI prototypes through production deployment.
- Strong problem-solving, communication, and collaboration skills, with the ability to work effectively alongside technical leads and distributed teams.
- Comfortable working within a follow-the-sun operating model and collaborating across time zones.
- A proactive, quality-focused mindset with an interest in building reusable systems rather than one-off AI experiments.
Benefits:
- Opportunity to build production-grade generative AI and agentic AI solutions in a healthcare and life-sciences environment.
- Hands-on exposure to modern AI technologies including Vertex AI Agent Builder, Claude, Gemini Enterprise, LangChain, LangGraph, CrewAI, and MCP.
- Opportunity to work with enterprise platforms including GCP, BigQuery, PostgreSQL, Salesforce Health Cloud, MuleSoft, and Java.
- Ability to influence reusable AI engineering standards, prompt libraries, agent patterns, evaluation frameworks, and development practices.
- Opportunity to work on meaningful patient-services use cases with tangible operational impact.
- Collaboration with distributed engineering and AI teams in a global, follow-the-sun environment.
- Inclusive culture that values innovation, diversity, continuous learning, collaboration, and professional development.
- Opportunity to contribute to a mission focused on improving healthcare and patient outcomes.
- Equal opportunity workplace committed to creating an environment where people with diverse backgrounds, experiences, and perspectives can thrive.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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