Senior AI Engineer

About DevSavant

DevSavant operates as a strategic partner for startups and growth-stage companies, transforming ambitious visions into actionable execution. They empower founders and leadership with product engineering and global staffing, supporting everything from initial prototypes and MVPs to building and scaling high-performing teams. With over 8 years in venture-backed ecosystems, DevSavant is a trusted ally for accelerating delivery, efficiently scaling teams, and achieving critical company milestones.

Key Responsibilities of a Senior AI Engineer

Agentic AI & Conversational Systems

• Design and deploy healthcare-focused Agentic AI systems for autonomous, multi-step tasks (e.g., appointment scheduling, eligibility verification, intake automation, triage routing).
• Architect and optimize LLM-powered conversational agents for voice and digital channels.
• Develop Retrieval-Augmented Generation (RAG) architectures to create contextual, domain-specific healthcare knowledge systems.
• Engineer robust prompt frameworks, safety guardrails, and evaluation pipelines specific to regulated healthcare environments.
• Continuously evaluate and improve agent performance, accuracy, and safety through structured experimentation and analytics.

Voice Automation & Infrastructure

• Architect advanced IVR modernization strategies and intelligent voice workflows.
• Optimize ASR (speech-to-text) and TTS (text-to-speech) systems for healthcare-grade accuracy and reliability.
• Integrate conversational AI into enterprise contact center platforms and CPaaS environments.
• Ensure seamless interoperability with EHR/EMR systems using healthcare standards such as FHIR and HL7.
• Design scalable, fault-tolerant architectures supporting high-availability healthcare operations.

Healthcare AI & Compliance

• Develop NLP pipelines for clinical document summarization, coding support, PHI detection, and structured data extraction.
• Architect HIPAA-compliant AI systems with secure data handling, encryption, and role-based access controls.
• Implement monitoring, observability, and analytics frameworks to measure operational efficiency and patient experience outcomes.
• Maintain alignment with evolving healthcare AI regulatory and compliance requirements.

Platform & Developer Enablement

• Contribute reusable AI templates for no-code and low-code deployment models.
• Build and maintain APIs and SDK integrations to enable rapid embedding of AI capabilities by enterprise customers.
• Collaborate cross-functionally with product, solution engineering, and co-creation teams to accelerate customer time-to-value.
• Mentor junior engineers and define best practices for deploying Agentic AI in regulated industries.
• Provide internal technical leadership on scalable AI architecture and deployment standards.

Required Qualifications

• 7+ years of experience in AI/ML engineering.
• 3+ years deploying AI solutions within healthcare or other regulated industries.
• Proven experience designing and deploying LLM architectures, including fine-tuning and advanced prompt engineering.
• Hands-on experience with voice automation systems (IVR, ASR, TTS).
• Experience integrating AI systems with enterprise platforms such as EHRs, CRMs, and contact centers.
• Strong Python expertise and experience with ML frameworks such as PyTorch, TensorFlow, and Hugging Face.
• Experience deploying AI solutions in cloud-native environments (AWS, GCP, or Azure).
• Strong understanding of secure system design and data privacy principles.

Nice to Have

• Experience building AI agents capable of autonomous, multi-step workflows.
• Deep knowledge of healthcare interoperability standards (FHIR, HL7).
• Experience working with vector databases and semantic search architectures.
• Familiarity with FDA software guidance (SaMD).
• Background in conversational analytics and customer experience optimization.
• Experience contributing to reusable AI frameworks that support low-code/no-code platforms.
• Proven track record of delivering production-grade AI systems in high-compliance environments.

Key skills/competency

• Agentic AI
• LLM Architectures
• Voice Automation
• Healthcare AI
• HIPAA Compliance
• Python
• PyTorch/TensorFlow
• Cloud-native (AWS, GCP, Azure)
• FHIR/HL7
• Prompt Engineering