Maya
Core Profile We are looking for a Lead Machine Learning Engineer to spearhead the machine learning engineering function for Maya's Marketing, Growth, and Personalization teams. In this role, you will lead, mentor, and elevate a team of machine learning engineers while driving close collaboration with data science, product, and cross-functional engineering partners. You operate as a technical authority who designs highly resilient, large-scale distributed ML systems—including next-generation agentic AI solutions and autonomous workflows—and masters compute optimization, balancing cloud economics, hardware constraints, and latency-throughput trade-offs. You will own complex mesh-facing service development, translate deep infrastructure trade-offs into business ROI, and ensure our marketing intelligence platforms scale efficiently and reliably. Nature of Work & Key Responsibilities Team Leadership & Engineering Growth: Lead and mentor Machine Learning Engineers across the Marketing, Growth, and Personalization domains. Guide staff and senior engineers on architectural blind spots, systemic debugging, and advanced software engineering paradigms. Agentic Solutions & Autonomous Workflows: Architect, deploy, and scale production-grade agentic AI systems (multi-agent frameworks, tool-using LLMs, autonomous decision-making loops, and goal-driven orchestration engines) to power hyper-personalized, real-time marketing journeys and automated growth campaigns. Cross-Functional Collaboration & Stakeholder Alignment: Partner closely with Product Owners, Data Scientists, and external engineering teams. Act as the bridge who translates complex technical infrastructure and architectural trade-offs to business leaders, aligning engineering costs directly with business ROI. Distributed ML Systems & Resilient Architecture: Architect and design highly resilient, large-scale distributed machine learning systems that anticipate, isolate, and mitigate catastrophic failure modes. Oversee complete system design and integration decisions. Mesh-Facing Service Development: Take complete ownership of mesh-facing service development to seamlessly integrate ML capabilities (such as real-time personalization, propensity scoring, and recommendation feeds) across disparate legacy and modern enterprise systems. Compute Optimization & Cloud Economics: Master deep compute optimization, managing cloud cost models, token economics for LLMs/agents, hardware-level constraints (GPUs/TPUs), and precise latency/throughput trade-offs. Make definitive strategic calls on leveraging managed cloud AI services versus building bespoke, in-house infrastructure. Full-Lifecycle MLOps & Production Standards: Set the standard for rigorous code reviews (platform, tool, utility, and model-level), enforce team coding standards, drive automated model monitoring deployment, and manage production deployment cycles from staging to high-availability production environments. Displayed Skill Mastery & Foundational Competencies Advanced Leadership & Architectural Mastery (Lead Level) Resilient Distributed Systems: Expert in designing fault-tolerant, large-scale distributed ML systems capable of handling high-volume traffic without degradation. Mesh & Service Integration: Extensive experience building microservices, RESTful APIs (FastAPI/Flask), and mesh-facing services that expose model outputs reliably. Cost & Hardware Optimization: Deep expertise in cloud economics, resource optimization for model training and inferencing, and hardware constraint tuning (GPUs/TPUs). Strategic Technology Judgment: Definitive ownership of end-to-end MLOps solutions, deciding when to adopt managed cloud AI services versus building custom components. Agentic & Generative AI Architecture: Deep expertise in building production-grade agentic workflows, multi-agent coordination frameworks (e.g., LangGraph, AutoGen, CrewAI), vector databases, retrieval-augmented generation (RAG), and tool-augmented LLM orchestration. Comprehensive Technical Foundation (Levels 1–4 Execution) Model Lifecycle & MLOps: Mastery of the complete model development lifecycle—from translating experimental data science models into production code, handling model staging/deployment, to implementing automated health checks, monitoring, and retraining triggers. Code Quality & Review Standards: Proficiency in reviewing complex DS and MLE code, establishing team-wide coding standards, and leveraging AI-assisted tools for code refactoring and baseline documentation. Core Stack Proficiency: Strong command of Python, SQL, PySpark/Spark, Bash, Docker, Kubernetes, Terraform/CloudFormation, and CI/CD pipelines (GitLab CI, GitHub Actions, Jenkins). Frameworks & Infrastructure: Familiarity with modern data and ML frameworks (Pandas, Scikit-learn, TensorFlow, PyTorch, MLflow) and cloud ecosystems (AWS, Databricks). Expected Results High-Performing Engineering Team: A mentored, highly productive team of ML engineers adhering to rigorous software engineering and MLOps standards. Resilient & Scalable Platforms: Mission-critical marketing, growth, and personalization ML systems that maintain high availability, low latency, and robust fault tolerance. Optimized Infrastructure Spend: A tightly managed balance between cloud computing costs (LLM Tokens, GPUs/TPUs, infrastructure overhead) and actual business value/ROI delivered. Seamless System Integration: Flawless mesh-facing service development that integrates advanced ML models cleanly into Maya’s broader technical ecosystem. Required Qualifications Education: Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Information Technology, or a quantitative discipline. Experience: 6+ years of hands-on experience building, deploying, and operating data pipelines and production machine learning systems. Leadership Track Record: 3+ years of experience leading technical initiatives, projects, or engineering teams, with proven success mentoring engineers and aligning cross-functional stakeholders. Infrastructure & MLOps Depth: 3+ years of experience designing CI/CD pipelines, distributed systems, microservices, and cloud-native infrastructure (AWS, GCP, or Azure). Agentic & GenAI Expertise: Demonstrated experience architecting and deploying LLM-based applications, multi-agent frameworks, vector databases, or automated decision-making workflows into production. Domain Context: Experience supporting AI/ML use cases in fintech, digital banking, e-commerce, or high-growth technology environments.
Maya
