Senior Staff AI Engineer

General Position Information

Reports Directly To (Title): Leader AI Engineering Matrix Reports To (Title): AVP AI & ML Platform engineering Direct Reports: No

Created / Last Revised: 4/20/2026 / Click here to enter a date.

Job Area: AI Engineering

Job Focus: AI Development

Job Category: Professional

Position Summary The Senior Staff AI Engineer serves as a technical authority and strategic leader in the application of advanced Generative AI technologies, driving enterprise-scale innovation across the healthcare ecosystem. This role is responsible for defining and evolving architectural direction, guiding complex AI initiatives, and ensuring the successful adoption of cutting ‑ edge AI capabilities that transform how healthcare solutions are designed and delivered.

Operating at a level of influence beyond a single team, the Senior Staff AI Engineer shapes the long-term technical roadmap for Generative AI, particularly across GCP Vertex AI, Gemini, agentic systems, and large-scale Retrieval-Augmented Generation (RAG) platforms. The role balances deep hands-on technical contribution with organization-wide technical stewardship, mentoring Staff and Senior engineers, and partnering with executive stakeholders to align AI strategy with business outcomes.

This position sits within a flagship innovation organization and is intended for a highly experienced engineer who consistently operates in ambiguous problem spaces, sets technical standards, and drives durable, scalable AI platforms across multiple initiatives.

Key Responsibilities

Technical Leadership & Architecture

- Define and evolve the architectural vision and technical standards for enterprise Generative AI platforms.
- Lead the design of large-scale, multi-team AI systems, ensuring robustness, scalability, security, and responsible AI practices.
- Serve as a final technical reviewer for critical architectural decisions impacting multiple teams or strategic initiatives.
- Establish best practices for LLM usage, RAG architectures, embeddings, evaluation, observability, and lifecycle management.

Advanced AI Systems Development

- Lead the development of enterprise-grade Generative AI solutions on Google Cloud Platform, with deep expertise in Vertex AI, Gemini, and emerging tooling.
- Architect and oversee sophisticated Retrieval-Augmented Generation (RAG) ecosystems grounded in proprietary healthcare data.
- Design and standardize data grounding strategies, schemas, and pipelines that connect LLMs with real-world, real-time data sources.
- Drive the exploration and production adoption of agentic AI systems capable of complex reasoning, orchestration, and autonomous task execution.
- Champion the enterprise adoption and evolution of interoperability frameworks such as the Model Context Protocol.

Organizational Influence & Strategy

- Partner with AI leadership, product leadership, and executive stakeholders to shape AI strategy and long-term investment priorities.
- Translate complex technical capabilities into clear business value propositions and architectural options for senior leadership.




- Influence roadmap planning across multiple teams by identifying shared platforms, reusable components, and systemic risks.

Mentorship & Engineering Excellence

- Act as a technical mentor and sponsor for Staff and Senior Engineers, guiding their growth toward higher-level technical impact.
- Foster a culture of engineering excellence through architecture reviews, design mentorship, and cross-team collaboration.
- Lead investigations into complex, ambiguous AI challenges and guide teams toward elegant, scalable solutions.

Education & Experience: Bachelor’s degree in computer science, Engineering, or a related field, or equivalent practical experience. Required 10+ years of qualified software engineering experience, with 5–7+ years focused on AI/ML or advanced applied machine learning systems.

Required Proven track record of architecting and influencing large, production-grade AI platforms across multiple teams or domains.

Required Licenses, Certifications, & Training:

Advanced cloud or AI certifications (e.g., GCP Professional ML Engineer, Cloud Architect) preferred.

Preferred Knowledge, Skills, Abilities, Behaviors:

Demonstrated growth mindset with the ability to proactively identify, evaluate, and drive adoption of emerging AI technologies, frameworks, and paradigms across teams and platforms in a rapidly evolving AI landscape.

Required Required Ability to independently operate in highly ambiguous problem spaces , define technical direction, and align engineering outcomes with long-term business strategy.

Required Expert-level knowledge and hands-on leadership experience with GCP Vertex AI, including generative AI models (e.g., Gemini), Vertex AI Studio, and enterprise deployment patterns; recognized as a subject matter expert across teams.

Required Advanced, architecture-level expertise in designing, scaling, and governing Retrieval-Augmented Generation (RAG) systems, including tradeoff analysis, evaluation frameworks, and long-term maintainability.

Required Proven experience defining enterprise standards and best practices for Vector Stores, embeddings, indexing strategies, and lifecycle management at scale.

Required Deep expertise in grounding strategies , including data modeling, schema design, and large-scale pipeline orchestration to ensure LLM factuality, traceability, and regulatory alignment.

Required Strong leadership-level experience with DevOps and CI/CD for AI/ML systems (MLOps), including platform design, governance, observability, and reliability across environments.

Required Extensive experience designing cloud-native architectures for AI platforms, including serverless, containerized, and hybrid solutions with a focus on scalability, resilience, and security.





Required Ability to define and review system and platform diagrams (e.g., Visio, architecture artifacts) that communicate complex architectures clearly to technical and executive audiences.

Required Strong working knowledge of the Model Context Protocol , with experience guiding its application to enable standardized, extensible interactions between LLMs and external tools and data sources

Required Advanced familiarity with Agentic AI frameworks and patterns (e.g., LangChain), including evaluation of autonomous and multi-agent systems for real-world enterprise use cases.

Required Broad and deep full-stack experience across enterprise technology ecosystems, with the ability to architect and integrate solutions involving:

- SQL and NoSQL databases (e.g., SQL Server, CosmosDB, MongoDB)
- ETL and large-scale data pipelines (e.g., GCP DataFlow, Azure Data Factory)
- Eventing and streaming platforms (e.g., Azure EventHub, GCP Pub/Sub, Kafka)
- Microservice and distributed systems (e.g., Docker/Kubernetes, Java, Python, NodeJS, C#)

Required Experience architecting and integrating AI solutions within CRM, ERP, eCommerce, or EMR/EHR systems , including enterprise integration patterns, security, and compliance considerations. Preferred Expert understanding of Agile methodologies and modern software development lifecycles , with the ability to influence process improvements across multiple teams.

Required Demonstrated ability to communicate complex technical concepts succinctly and persuasively to engineers, product leaders, and executive stakeholders, both verbally and in writing.

Required Ability to lead engineering communities of practice , technical forums, or internal events focused on AI, software engineering excellence, or platform evolution.

Required Exceptional problem-solving and analytical skills , with a history of resolving systemic, high‑impact technical challenges.

Required Advanced troubleshooting expertise, including log analysis, performance tuning, and load testing in large, distributed AI systems.

Required Ability to lead through influence , guiding multiple engineers or teams toward outcomes without direct authority.

Required Strong ability to understand and shape technical context , including codebases, architectures, and their direct relationship to business objectives and long-term strategy.

Required Travel Required Check the frequency of travel required of the employee to perform the essential functions of the job. ☐

No Travel: The job does not require any travel.

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Occasional Travel: The job may require travel from time- to-time, but not on a regular basis.

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The job may require up to 25% travel.

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The job may require up to 50% travel.

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The job may require up to 75% travel.

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The job may require 76% or more travel.

My signature below acknowledges that I have read the above and agree that I can perform the responsibilities and meet the requirements. I also understand that this may change at any given time based on organizational or department needs.

SIGNOFF & ACKNOWLEDGEMENT

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📌 AI Engineer (Hyderabad)
🏢 HCA Healthcare - India
📍 Hyderabad