Senior Manager – AI Engineer

Role Overview

• Define the technical vision and architecture for AI and ML solutions, including LLM based applications, conversational voice AI, chatbot platforms, and AI augmented BI systems
• Oversee development of training data pipelines and datasets for fine tuning, evaluation, and inference at enterprise scale
• Establish enterprise standards for model performance monitoring, drift detection, retraining, and lifecycle governance
• Drive implementation of vector embeddings, semantic search, and AI orchestration frameworks
• Provide engineering leadership for backend services (APIs, microservices) enabling scalable AI capabilities across the enterprise
• Oversee development of scalable data pipelines supporting ingestion, transformation, and real time inference workloads
• Drive integration of AI capabilities into enterprise platforms, including customer facing voice and chat systems and internal analytics environments
• Ensure solutions meet enterprise standards for scalability, reliability, performance, and security
• Define and govern model lifecycle management practices including versioning, deployment, rollback, and compliance
• Lead the development of enterprise AI platforms and infrastructure for model hosting, orchestration, and scaling
• Establish CI and CD standards and deployment frameworks for AI systems across engineering teams
• Build and oversee observability layers to monitor system performance, model behavior, and operational health
• Set direction for AI safety and responsible AI practices, including guardrails for bias mitigation, hallucination reduction, and policy adherence
• Set and drive enterprise AI strategy aligned to technology vision, platform evolution, and long-term organizational priorities
• Lead alignment across a highly matrixed organization, influencing engineering, product, analytics, and business leadership
• Serve as a trusted advisor to executive leadership, communicating AI strategy, technical trade-offs, risks, and business impact
• Own AI investment strategy, including prioritization, funding alignment, and resource allocation across initiatives
• Drive enterprise-wide AI adoption by establishing scalable enablement models across engineering and business teams
• Define and execute capability uplift strategies, including upskilling engineers, promoting best practices, and enabling self-service AI development
• Champion innovation by introducing emerging AI technologies, tools, and solution patterns to accelerate experimentation and delivery
• Establish and govern AI vendor and partner strategy, including evaluation, selection, negotiation, and performance oversight
• Oversee SOW development and partner with product and finance leadership to manage budgets, forecasts, and investment planning
• Act as the primary interface between engineering and executive leadership, ensuring transparency, accountability, and delivery outcomes
• Influence enterprise architecture, engineering standards, and AI governance frameworks

Requirements

• Extensive experience leading engineering or AI and ML organizations within large scale enterprise environments
• Demonstrated ability to operate at a senior leadership level, influencing executive stakeholders and enterprise strategy
• Proven experience owning or driving technology investment strategy, budgeting, and resource allocation
• Experience leading transformation initiatives and driving adoption of emerging technologies across organizations
• Experience building and scaling engineering platforms, systems, or organizational capabilities
• Experience within healthcare, health insurance, or regulated healthcare environments, with strong understanding of compliance, data privacy, and domain specific challenges
• Deep expertise in software engineering fundamentals (SDLC, architecture, distributed systems design)
• Proficiency in one or more programming languages (Python, C#, Java, etc.)
• Experience building data pipelines and working with structured and unstructured data
• Hands on experience with AI and ML frameworks, platforms, or applied AI systems
• Strong understanding of APIs, microservices, and cloud-based architectures
• Experience with cloud platforms (Azure, AWS, or GCP)
• Familiarity with databases (SQL / NoSQL)
• Experience leading vendor strategy, including evaluation, selection, and delivery governance.
• Experience defining or leading enterprise AI strategy or platforms at scale (Preferred)
• Hands on experience with LLMs, prompt engineering, or fine-tuning models (Preferred)
• Experience building conversational AI (voice and chat) ecosystems (Preferred)
• Experience with AI augmented analytics or business intelligence platforms (Preferred)
• Experience with vector databases, embeddings, and semantic search (Preferred)
• Familiarity with MLOps, observability, and model monitoring frameworks (Preferred)
• Experience implementing responsible AI, governance, and risk management practices (Preferred)
• Experience operating at Director or VP level or equivalent leadership scope (Preferred)
• Experience in healthcare, analytics, or enterprise data platforms (Preferred)
• Exposure to tools such as Databricks, Spark, or real time analytics systems (Preferred)

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Google Cloud Platform
• Java
• Microservices
• NoSQL
• Python
• SDLC
• Spark
• SQL

Benefits

• medical, dental, and vision coverage
• paid time off
• retirement savings options
• wellness programs
• comprehensive benefits package