Engineering Manager
Brillio
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Job summary
Brillio is seeking an experienced Engineering Manager – AI/GenAI to lead the engineering and delivery of complex AI and Generative AI solutions for Healthcare clients. This technical leadership role requires a strong foundation in software engineering, AI/ML, cloud technologies, and modern engineering practices.
The role leads cross-functional teams, provides technical direction, and ensures AI solutions are scalable, secure, reliable, and production-ready while engaging with senior client
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, AI, Data Science, or related.
- 12+ years of technology/engineering experience.
- 5+ years in engineering leadership or management.
- Experience leading AI/ML and/or Generative AI programs.
- Strong cloud experience across Azure, AWS, or GCP.
- Experience with Agile software development and DevOps practices.
- Excellent communication, leadership, and stakeholder management skills.
Responsibilities
- Lead architecture, engineering, development, and production deployment of enterprise AI/GenAI solutions.
- Provide technical leadership across AI/ML, GenAI, data engineering, cloud, APIs, and application development.
- Guide engineering teams on architecture, design patterns, technology selection, scalability, performance, security, and reliability.
- Review technical designs and ensure engineering solutions meet enterprise architecture and quality standards.
- Drive technical decision-making and resolve complex engineering challenges.
- Ensure AI prototypes and POCs can transition into scalable, production-grade solutions.
- Own technical execution from discovery to production support.
- Develop engineering plans, milestones, dependencies, and release strategies.
- Coordinate across multiple engineering workstreams and geographically distributed teams.
- Ensure timely resolution of technical blockers and production issues.
Skills
Education
Tools
Job description
Brillio is seeking an experienced Engineering Manager – AI/GenAI to lead the engineering and delivery of complex Artificial Intelligence and Generative AI solutions for our Healthcare clients.
This is a technical engineering leadership role requiring a strong foundation in software engineering, AI/ML, cloud technologies, and modern engineering practices. The Engineering Manager will be responsible for leading cross-functional engineering teams, providing technical direction, managing end-to-end execution, and ensuring AI solutions are scalable, secure, reliable, and production-ready.
The ideal candidate will have experience managing AI/ML and Generative AI projects in a consulting or client-facing environment, preferably within Healthcare, and should be comfortable engaging with both senior client stakeholders and deeply technical engineering teams.
- Lead the architecture, engineering, development, and production deployment of enterprise AI and Generative AI solutions.
- Provide technical leadership across AI/ML, GenAI, data engineering, cloud, APIs, and application development.
- Guide engineering teams on architecture, design patterns, technology selection, scalability, performance, security, and reliability.
- Review technical designs and ensure engineering solutions meet enterprise architecture and quality standards.
- Drive technical decision-making and resolve complex engineering challenges.
- Ensure AI prototypes and POCs can successfully transition into scalable, production-grade solutions.
Lead engineering initiatives involving technologies and use cases such as:
- Generative AI applications
- Retrieval-Augmented Generation (RAG)
- AI Agents / Agentic AI
- Conversational AI
- Natural Language Processing (NLP)
- Machine Learning and Predictive Analytics
- Prompt Engineering
- Embeddings and Vector Databases
- Model evaluation and optimization
- AI orchestration frameworks
- MLOps / LLMOps
- Responsible AI and AI governance
Experience working with platforms and technologies such as Azure OpenAI, OpenAI APIs, AWS Bedrock, Google Vertex AI, Databricks, MLflow, LangChain, LlamaIndex, Hugging Face, or equivalent technologies is highly desirable.
Lead AI initiatives supporting Healthcare use cases such as:
- Clinical and healthcare data analytics
- Member and patient engagement
- Provider analytics
- Claims analytics
- Care management
- Pharmacy and medication management
- Contact center transformation
- Knowledge management
- Clinical or operational workflow automation
- AI-powered search and knowledge assistants
- Predictive analytics
Ensure solutions appropriately address Healthcare-specific requirements around data privacy, security, HIPAA compliance, explainability, governance, and responsible use of AI.
Knowledge of healthcare data and interoperability standards such as FHIR, HL7, EDI, claims, member, provider, pharmacy, and clinical data is highly desirable.
- Lead and mentor cross-functional teams of AI Engineers, ML Engineers, Data Scientists, Data Engineers, Software Engineers, Architects, and DevOps/MLOps Engineers.
- Provide technical coaching and career development to engineering team members.
- Establish clear engineering standards and expectations.
- Conduct architecture and design reviews.
- Drive code quality, peer reviews, automated testing, and engineering best practices.
- Build a culture of engineering excellence, accountability, experimentation, and continuous improvement.
- Manage globally distributed onshore and offshore engineering teams.
- Python
- PyTorch / TensorFlow
- Scikit-learn
- MLflow
- LLMs
- Agentic AI
- Embeddings
- Model evaluation and guardrails
- AWS
- Databricks
- Snowflake
- Spark
- SQL
- Data Lakes / Lakehouse
- Python
- Java
- REST APIs
- Microservices
- Event-driven architectures
- React or modern front-end frameworks
- Docker
- CI/CD
- Terraform
- Model deployment and monitoring
- Observability
- Serve as a senior technical point of contact for Healthcare clients.
- Translate business requirements and AI use cases into technical solutions and engineering roadmaps.
- Participate in client workshops, architecture discussions, technical reviews, and executive governance meetings.
- Clearly communicate technical decisions, risks, dependencies, and trade-offs to both technical and non-technical stakeholders.
- Build credibility and trusted relationships with client engineering and technology leadership.
- Manage client expectations and proactively address technical or delivery concerns.
- Partner with Product Managers, Business Analysts, Architects, and client stakeholders to prioritize features and technical requirements.
- Identify opportunities where AI and automation can solve additional client business problems.
- Own technical execution from discovery and architecture through development, testing, deployment, and production support.
- Develop engineering plans, milestones, dependencies, and release strategies.
- Ensure projects meet committed timelines, quality standards, and business outcomes.
- Manage technical risks and dependencies proactively.
- Drive Agile/Scrum engineering practices.
- Monitor engineering velocity, quality, defects, production stability, and technical debt.
- Coordinate across multiple engineering workstreams and geographically distributed teams.
- Ensure timely resolution of technical blockers and production issues.
The Engineering Manager will ensure AI solutions are built for enterprise-scale production environments, including:
- Model deployment and versioning
- Model and prompt evaluation
- Data and model drift monitoring
- LLM observability
- Cost and latency optimization
- CI/CD for AI/ML workloads
- Data privacy
- AI guardrails
- Responsible AI
- Ensure AI solutions follow appropriate governance and responsible AI practices.
- Address risks related to hallucination, bias, explainability, data privacy, security, and inappropriate model outputs.
- Implement appropriate guardrails and validation mechanisms.
- Partner with security, legal, compliance, architecture, and client teams when deploying AI solutions involving sensitive Healthcare data.
- Ensure proper documentation and traceability of AI systems.
- Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related technical discipline.
- 12+ years of overall technology/engineering experience.
- 5+ years of engineering leadership or engineering management experience.
- Demonstrated experience leading AI/ML and/or Generative AI engineering programs.
- Strong software engineering and architecture foundation.
- Strong understanding of modern AI/ML and Generative AI architecture.
- Experience managing multidisciplinary engineering teams.
- Strong cloud experience across Azure, AWS, or GCP.
- Experience with Agile software development and DevOps practices.
- Excellent communication, leadership, and stakeholder management skills.
- Generative AI / LLM solutions
- Strong Python and software engineering understanding
- Cloud-native architecture
- APIs and Microservices
- MLOps / LLMOps
- Architecture and design reviews
- Technical problem-solving
- Team leadership and mentoring
- Client and stakeholder management
- Healthcare industry experience, particularly with payer, provider, pharmacy, or life sciences clients.
- Experience delivering technology solutions in a consulting or IT services organization.
- Experience managing client-facing AI transformation programs.
- Understanding of HIPAA and Healthcare data privacy requirements.
- Knowledge of FHIR, HL7, EDI, claims, clinical, member, provider, or pharmacy data.
- Experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, Databricks, or similar enterprise AI platforms.
- Experience implementing RAG and Agentic AI architectures.
- Experience taking GenAI solutions from POC to enterprise production.
We are looking for someone who is more than a project manager. The successful candidate should be a strong technical engineering leader who can challenge architecture decisions, guide engineers, engage credibly with client technology leaders, and take ownership of delivering AI solutions into production.
The individual should be equally comfortable discussing LLM architecture and engineering challenges with developers and discussing delivery status, risks, business outcomes, and strategy with client executives.
- Successful delivery of AI/GenAI programs
- On-time and predictable engineering execution
- Production stability and solution quality
- Engineering productivity and velocity
- Technical quality and reduction of technical debt
- Client satisfaction
- Successful transition of AI solutions from POC to production
- AI model/application performance and reliability
- Development and retention of high-performing engineering teams
- Strong collaboration with client technology leadership
- Identification of opportunities to expand AI adoption within client organizations
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