The company is looking to solve critical business challenges across IT and all enterprise functions (Sales, Marketing, Finance, HR, Legal, etc.) by leveraging AI-powered solutions. The goal is to translate ambiguous problems into concrete AI solution designs, build core components of these solutions, and ensure their successful deployment and measurable impact.
Requirements
- 10+ years of experience in software engineering, distributed systems, or enterprise architecture, including at least 5 years focused on leading AI/ML engineering or platform development
- Proven expertise in designing and building complex, enterprise-grade AI/ML platforms and applications, ideally across multiple domains (e.g., customer support, finance, sales)
- Deep practical understanding of the full AI lifecycle for developing assistants, agents, and applications, including feature engineering, RAGs, model training, evaluation, validation, deployment, and monitoring
- Extensive hands-on experience with distributed systems architecture, streaming data platforms, data lakes, and real-time decision engines
- Generative AI & Large Language Models (LLMs): Direct hands-on experience with Generative AI technologies, including Large Language Models (LLMs), multi-modal models, RAGs (Retrieval-Augmented Generation), and agentic AI systems. Familiarity with techniques for LLM alignment, fine-tuning, and responsible deployment
- Proficiency in modern AI/ML frameworks (e.g., TensorFlow, PyTorch, JAX) and cloud AI/ML platforms (e.g., AWS SageMaker, Google Cloud AI Platform/Vertex AI, Azure ML)
- Strong programming skills in languages such as Python, Java, or Go
Responsibilities
- Applied AI Solution Design & Architecture: Deeply understand complex business problems and strategic objectives across various enterprise functions. Break down ambiguous AI problems into concrete, actionable AI solution designs, including identifying the appropriate AI models, data requirements, and integration points for end-to-end AI applications
- Hands-on Development & Implementation: Lead the hands-on development and implementation of key components of the AI assistant, agent, app, supporting both traditional and Generative AI model development, deployment, and real-time inference systems. Drive the successful integration of experimental AI technologies into production, showcasing tangible business value through rapid prototyping and measurable results
- System Design & Optimization: Contribute significantly to the detailed design of large-scale, distributed AI/ML systems, ensuring performance, reliability, security, and developer-friendliness. Optimize existing systems for scalability, efficiency, and maintainability, ensuring the platform's ability to handle massive scale data and inference requests, optimizing for low latency and high throughput for real-time AI applications
- Rapid Experimentation & Integration: Evaluate and integrate new AI tools, frameworks, SDKs, and cloud solutions into the platform, ensuring alignment with architectural strategy and engineering needs. Lead proof-of-concepts (POCs) for emerging AI innovations and drive their integration into production through long-term architectural evolution
- Architectural Adherence & Best Practices: Champion and enforce design standards, patterns, and best practices for scalable and secure development of AI assistants, agents, and applications across various teams
- Responsible AI & Governance: Implement features and practices that ensure AI systems comply with responsible AI principles, data governance, privacy laws, security policies, and ethical AI frameworks.
- MLOps and Automation: Lead the implementation and continuous improvement of MLOps pipelines, including automated model training, versioning, deployment, and monitoring, to streamline the AI lifecycle. Design and implement automated testing strategies for AI models and platform components to ensure model quality, robustness, and drift detection
Other
- Provide technical leadership and mentorship to other AI and ML engineers, fostering a culture of engineering excellence, innovation, and hands-on experimentation within the team and across the company
- Partner effectively with executive leadership, data science teams, engineering, product stakeholders, and business leaders to translate complex business use-cases into scalable, production-grade AI solutions. Act as a go-to expert for complex AI engineering challenges, providing technical guidance and hands-on support
- Excellent communication skills with a track record of influencing technical and cross-functional stakeholders at VP+ levels
- System-Level Thinking: Demonstrated ability to think at a system level, understanding complex interdependencies within distributed AI architectures and optimizing end-to-end performance
- Master's degree or Ph.D. in Computer Science, Machine Learning, or a related technical field or equivalent military experience required
- Experience contributing to open-source AI projects or publications in top-tier AI conferences
- Prior experience in a technical consulting or solutions architecture role, bridging technical teams and senior business stakeholders
- You are a systems thinker and AI evangelist with the ability to scale complex ideas into architecture that lasts. You thrive in ambiguity, excel at influencing across technical and business domains, and are driven to make AI core to the fabric of a company's digital transformation. You are a hands-on AI engineering leader with a passion for building robust, scalable AI platforms and the ability to translate strategic business needs into innovative, impactful AI solutions.
