AI Engineer
HCA Healthcare India19 days ago
Hyderābād, IndiaStaff+
Responsibilities
- Define the architectural vision, technical standards, and long-term roadmap for enterprise Generative AI platforms.
- Lead the design and development of large-scale, multi-team AI systems using Vertex AI, Gemini, agentic systems, and RAG architectures.
- Design data grounding strategies, schemas, embeddings, vector-store practices, evaluation frameworks, and pipelines connecting LLMs with real-time data.
- Establish standards for observability, lifecycle management, governance, reliability, security, and responsible AI practices.
- Review critical architecture decisions and communicate complex system designs to technical and executive audiences.
- Partner with AI leadership, product leadership, and executives to shape AI strategy, investment priorities, and business outcomes.
- Mentor and sponsor Staff and Senior Engineers and foster engineering excellence through architecture reviews and technical communities.
- Lead investigations into ambiguous, high-impact AI and distributed-systems challenges and guide teams toward scalable solutions.
Requirements
- Bachelor ’s degree in computer science, engineering, or a related field, or equivalent practical experience.
- 10+ years of professional software engineering experience, including 5–7+ years focused on AI/ML or advanced applied machine learning systems.
- Proven experience architecting and influencing large, production-grade AI platforms across multiple teams or domains.
- Expert hands-on experience with GCP Vertex AI, Gemini, Vertex AI Studio, and enterprise AI deployment patterns.
- Advanced architecture-level expertise with Retrieval-Augmented Generation systems, evaluation, vector stores, embeddings, indexing, and lifecycle management.
- Deep experience with data modeling, schema design, pipeline orchestration, LLM grounding, factuality, traceability, and regulatory alignment.
- Leadership-level experience with DevOps, CI/CD, MLOps, platform governance, observability, and reliability across environments.
- Experience designing cloud-native, serverless, containerized, and hybrid AI architectures.
- Strong knowledge of the Model Context Protocol and agentic AI frameworks and patterns such as LangChain.
- Broad full-stack experience with SQL and NoSQL databases, ETL and data pipelines, eventing and streaming platforms, microservices, and distributed systems.
- Experience integrating AI solutions with CRM, ERP, eCommerce, or EMR/EHR systems is preferred.
- Advanced cloud or AI certifications such as GCP Professional ML Engineer or Cloud Architect are preferred.
- Experience with architecture diagrams, troubleshooting, performance tuning, load testing, Agile software development lifecycles, and leading through influence.
- Strong communication, analytical, problem-solving, mentorship, and executive-stakeholder collaboration skills.
Benefits
- Occasional travel may be required.
Tech Stack
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