AI/ML Ops / Software Engineer (Tech Lead)

OVERVIEW

ABOUT MODERN TREASURY

KEY RESPONSIBILITIES

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  End-to-End AI/ML Lifecycle Management:
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  Lead and optimize the ML SDLC, ensuring a smooth transition from model research to deployment, integrating CI/CD pipelines, monitoring, and scaling.
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  Oversee the full lifecycle of AI systems, from model development and experimentation to testing, deployment, and continuous optimization.
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  Collaboration with AI Product Researchers:
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  Collaborate closely with AI Product Researchers to translate research outcomes into scalable, production-ready AI models.
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  Build amplifying harnesses and tools to improve research-to-production cycles, allowing researchers to rapidly iterate on models.
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  AI/ML Infrastructure and Operations:
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  Architect and manage robust AI/ML Ops frameworks that handle the training, serving, monitoring, and lifecycle management of models, enabling fast iteration and high reliability.
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  Establish best practices for AI/ML infrastructure, ensuring operational excellence in production environments.
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  Build Scalable and Distributed Systems:
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  Lead the design of scalable architectures that support both real-time inference and large-scale model training.
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  Optimize performance and scalability across distributed AI systems, leveraging cloud technologies (e.g., AWS SageMaker, EKS, and distributed computing frameworks).
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  Drive Innovation in AI Model Deployment:
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  Lead innovation initiatives, pushing the envelope on AI-driven systems such as LLMs, transformers, and recommendation engines.
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  Enable MLOps best practices, incorporating automation for model versioning, rollback, and monitoring to ensure robustness and compliance in production environments.
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  Cross-functional Leadership:
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  Partner with engineering, product, and data teams to ensure that AI solutions are fully integrated into product features, driving measurable business outcomes.
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  Mentor and lead engineering teams, establishing a strong culture of technical excellence, collaboration, and innovation.
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  Research into Production:
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  Facilitate the seamless integration of research-grade AI models into production, ensuring performance, scalability, and alignment with business requirements.
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  Develop tools and infrastructure that empower researchers to experiment with and deploy cutting-edge models faster.

QUALIFICATIONS

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  10+ years of experience in AI engineering or machine learning infrastructure, with significant experience leading end-to-end AI model development and deployment in production environments.
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  Expertise in AI/ML Ops and ML SDLC:
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  Strong experience with modern MLOps frameworks and practices, including automated model deployment, monitoring, and lifecycle management.
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  Deep understanding of the ML SDLC, with hands-on experience building pipelines for scalable model serving, retraining, and testing.
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  Collaborative AI Research to Production Experience:
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  Proven ability to work closely with AI Product Researchers to transform cutting-edge research into scalable, real-world AI solutions.
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  Experience building tools and amplifying harnesses that streamline the transition from research models to full-scale deployment.
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  Expert in Modern AI Architectures:
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  Deep expertise in LLMs, transformers, and advanced recommendation systems, with hands-on experience in building and optimizing models using open-source frameworks such as PyTorch and TensorFlow.
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  Proficiency in optimizing and deploying AI models on cloud platforms, especially AWS, leveraging tools like SageMaker, EC2, and Kubernetes.
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  Leadership and Technical Expertise:
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  Proven leadership experience in managing engineering teams, guiding architecture decisions, and driving AI strategy at the enterprise level.
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  Strong coding and automation skills (Python, ML frameworks), with a focus on productionizing machine learning models using MLOps best practices.
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  Cross-functional Communication:
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  Excellent communication skills, with the ability to collaborate with AI researchers, engineers, product managers, and business stakeholders to deliver impactful AI solutions.
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  A strategic mindset with the ability to lead and mentor technical teams, driving a culture of collaboration and innovation.