AI Engineer

  AI Platform Admin (M365, copilot Studio) Manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance).
·    AI Reusable Utility Develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases.
·    AI Common Infrastructure, Framework & Observability Architect (AWS and Azure) Designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations.
Core Responsibilities
Advanced Solution Development
•    Build, deploy, and optimize LLM based, multimodal, and predictive AI models.
•    Develop intelligent automation to streamline workflows and reduce operational friction.
•    Implement NLP, conversational AI, and real time generative systems across modalities.
AI System Integration & Full Stack Engineering
•    Oversee AI system integration with enterprise platforms, cloud services, APIs, and data pipelines.
•    Architect and maintain production grade ML infrastructure, including CI/CD and monitoring.
•    Operationalize AI/ML models in cloud environments such as AWS, Azure, or GCP.
Governance, Quality & Compliance
•    Ensure AI solutions meet regulatory and internal governance requirements.
•    Implement comprehensive testing frameworks across all system layers.
Collaboration & Mentorship
•    Partner with cross functional teams to deliver robust, production ready AI solutions.
•    Mentor engineers in AI architecture, cloud engineering, and advanced model integration.
•    Provide technical mentorship in prompt engineering, fine tuning, and responsible AI practices.
Required Skills and Experience
•    8+ years of professional experience in data engineering, machine learning engineering, software engineering, or related fields. All relevant experience including work, education, transferable skills, and military experience will be considered.
•    5+ years of experience deploying and maintaining ML/AI models in production at enterprise scale.
•    Advanced proficiency in Python, Scala, or comparable languages.
•    Expertise with cloud architectures (AWS, Azure, GCP), including ML focused services (e.g., SageMaker, AzureML).
•    Strong experience with orchestration and containerization tools such as Kubernetes, Docker, Airflow, etc.
•    Deep understanding of machine learning algorithms, deep learning frameworks (PyTorch, TensorFlow), and NLP technologies.
•    Demonstrated experience with generative AI, multimodal systems, LLM fine tuning, and prompt engineering.
•    Strong SQL and experience with cloud native data ecosystems.
•    Proven ability to architect, deploy, and monitor complex AI/ML solutions in production.
•    Excellent communication, collaboration, and technical leadership skills.

Preferred Skills and Experience
•    Advanced degree in Computer Science, AI, Data Science, Engineering, or a related field.
•    Experience in healthcare, health services, or insurance.
•    Expertise in RAG pipelines, vector databases, and enterprise knowledge systems.
•    Experience with real time streaming systems, edge AI, or high performance model serving.
•    Background in full stack engineering incorporating AI driven user experiences.
•    Professional certifications in cloud architectures or ML/AI technologies.