Manager - Product Development

HCA Healthcare - India; Hyderabad; On-site; INR 3,000,000 - 5,400,000; Full time

Job summary

HCA Healthcare - India seeks a Senior Staff AI Engineer to serve as a technical authority for enterprise Generative AI initiatives. This role defines architectural direction, guides multi-team AI programs, and mentors senior engineers, while aligning AI strategy with business goals across healthcare platforms.

The candidate will lead initiatives around Vertex AI, Gemini, and RAG ecosystems, balancing hands-on work with strategic stewardship in a flagship innovation setting.

Qualifications

• Bachelor's degree in computer science, engineering, or related field; practical experience accepted.
• 10+ years of software engineering experience; 5–7+ years in AI/ML or advanced applied ML systems.
• Proven track record architecting and influencing large, production-grade AI platforms across teams.

Responsibilities

• Define and evolve architectural vision for enterprise Generative AI platforms.
• Lead design of large-scale, multi-team AI systems with robustness, security, and responsible AI practices.
• Mentor Staff/Senior engineers and align AI strategy with business objectives.
• Partner with AI leadership and executives to shape long-term AI strategy and investments.
• Drive standards for LLM usage, RAG architectures, embeddings, and observability.

Skills

Vertex AI

RAG systems

MLOps / CI-CD

Cloud architecture

Leadership

Stakeholder comms

LangChain

Education

Bachelors in CS/Engineering

Tools

Kubernetes

Docker

SQL/NoSQL

Data pipelines

Job description

General Position Information

Reports directly to (Title):Leader AI Engineering Matrix reports to (Title):AVP AI & ML Platform engineering Direct Reports:Individual contributor; no direct reports Created / Last Revised:- Job Code:-

General Position Information

Reports directly to (Title):Leader AI Engineering Matrix reports to (Title):AVP AI & ML Platform engineering Direct Reports:Individual contributor; no direct reports Created / Last Revised:- Job Code:-

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.

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.
• 10+ years of professional software engineering experience, with 5–7+ years focused on AI/ML or advanced applied machine learning systems.
• Proven track record of architecting and influencing large, production-grade AI platforms across multiple teams or domains.
• Experience working across multiple stakeholders and managing recurring deliverables in a deadline-driven environment.

Must Have Skills

• 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.
• Ability to independently operate in highly ambiguous problem spaces, define technical direction, and align engineering outcomes with long-term business strategy.
• 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.
• Advanced, architecture-level expertise in designing, scaling, and governing Retrieval-Augmented Generation (RAG) systems, including tradeoff analysis, evaluation frameworks, and long-term maintainability.
• Proven experience defining enterprise standards and best practices for Vector Stores, embeddings, indexing strategies, and lifecycle management at scale.
• Deep expertise in grounding strategies, including data modeling, schema design, and large-scale pipeline orchestration to ensure LLM factuality, traceability, and regulatory alignment.
• Strong leadership-level experience with DevOps and CI/CD for AI/ML systems (MLOps), including platform design, governance, observability, and reliability across environments.
• Extensive experience designing cloud-native architectures for AI platforms, including serverless, containerized, and hybrid solutions with a focus on scalability, resilience, and security.
• Ability to define and review system and platform diagrams (e.g., Visio, architecture artifacts) that communicate complex architectures clearly to technical and executive audiences.
• 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.
• Advanced familiarity with Agentic AI frameworks and patterns (e.g., LangChain), and evaluation of autonomous and multi-agent systems for real-world enterprise use cases.
• 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).
• Experience architecting and integrating AI solutions within CRM, ERP, eCommerce, or EMR/EHR systems, including enterprise integration patterns, security, and compliance considerations.
• Expert understanding of Agile methodologies and modern software development lifecycles, with the ability to influence process improvements across multiple teams.
• Demonstrated ability to communicate complex technical concepts succinctly and persuasively to engineers, product leaders, and executive stakeholders, both verbally and in writing.
• Ability to lead engineering communities of practice, technical forums, or internal events focused on AI, software engineering excellence, or platform evolution.
• Exceptional problem-solving and analytical skills, with a history of resolving systemic, high-impact technical challenges.
• Advanced troubleshooting expertise, including log analysis, performance tuning, and load testing in large, distributed AI systems.
• Ability to lead through influence, guiding multiple engineers or teams toward outcomes without direct authority.
• Strong ability to understand and shape technical context, including codebases, architectures, and their direct relationship to business objectives and long-term strategy.

Nice To Have Skills

• Experience implementing and governing Responsible AI, AI safety, model evaluation frameworks, and compliance controls within enterprise environments.
• Experience leading cloud transformation initiatives involving AI/ML platforms, data modernization, and large-scale enterprise application integration.
• Hands-on experience with healthcare data platforms, Electronic Health Records (EHR/EMR), healthcare interoperability standards, or regulated industry environments.
• Experience building and scaling enterprise AI Centers of Excellence (CoE), Communities of Practice, or technical innovation programs across distributed teams.
• Relevant industry certifications in Google Cloud Platform (GCP), Artificial Intelligence/Machine Learning, Cloud Architecture, Kubernetes, or related technologies.

Licenses, Certifications & Training

N/A

Knowledge, Skills, Abilities, Behaviors

• Strong strategic and architectural mindset with the ability to define technical vision, influence enterprise-wide AI initiatives, and align technology decisions with business objectives.
• Exceptional leadership, collaboration, and stakeholder management skills, with the ability to communicate complex AI and technical concepts effectively to engineers, product teams, and executive leadership.
• Demonstrated ability to lead through influence, mentor senior engineering talent, and foster a culture of innovation, engineering excellence, continuous learning, and knowledge sharing.
• Strong analytical, problem-solving, and decision-making capabilities with the ability to navigate ambiguity, assess technical risks, and develop scalable, secure, and sustainable AI solutions.
• Results-oriented, adaptable, and customer-focused professional with a growth mindset, strong sense of ownership, and commitment to delivering high-quality, responsible, and business-driven AI outcomes.