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Hybrid Senior Engineering Manager – AI

at FourKites, Inc.
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About the role

  • Define and execute the technical strategy for AI/ML initiatives across multiple product areas
  • Oversee the design and architecture of scalable ML systems, from data pipelines to model deployment
  • Drive decisions on technology stack, frameworks, and infrastructure for AI/ML workloads
  • Ensure engineering excellence through code reviews, design reviews, and technical mentorship
  • Lead, mentor, and grow a team of 15+ AI engineers, data scientists, and software engineers
  • Build high-performing teams through hiring, performance management, and career development
  • Foster a culture of innovation, collaboration, and continuous learning
  • Conduct regular 1:1s, performance reviews, and career development conversations
  • Partner with Product Management to define AI product roadmap and priorities
  • Translate business objectives into technical initiatives and measurable outcomes
  • Manage multiple concurrent AI/ML projects from conception to production deployment
  • Establish and track KPIs for team performance, model quality, and system reliability
  • Work closely with Data Science, Product, Design, and other engineering teams and communicate technical concepts to non-technical stakeholders
  • Represent engineering in executive discussions and strategic planning sessions and build relationships with external partners
  • Drive alignment across teams on AI ethics, responsible AI practices, and governance
  • Establish best practices for ML model development, testing, and deployment and implement MLOps practices for CI/CD of ML models
  • Ensure compliance with data privacy regulations and AI governance policies
  • Drive improvements in model monitoring, A/B testing, and experimentation frameworks
  • Manage engineering budget and resource allocation

Requirements

  • 13+ years of software engineering experience, with 5+ years focused on ML/AI systems
  • 5+ years of engineering management experience, including managing managers
  • Proven track record of shipping ML products at scale in production environments
  • Experience with full ML lifecycle: data collection, feature engineering, model training, deployment, and monitoring
  • Deep understanding of machine learning algorithms, deep learning, and statistical methods
  • Proficiency in ML frameworks (TensorFlow, PyTorch, JAX) and programming languages (Python, Scala, Java)
  • Experience with distributed computing frameworks (Spark, Ray) and cloud platforms (AWS, GCP, Azure)
  • Knowledge of MLOps tools and practices (Kubeflow, MLflow, Airflow, Docker, Kubernetes)
  • Understanding of data engineering, ETL pipelines, and big data technologies
  • Demonstrated ability to build and scale engineering teams
  • Strong communication skills with ability to influence at all levels of the organization
  • Experience driving technical strategy and making architectural decisions
  • Track record of successful cross-functional collaboration and stakeholder management
  • Ability to balance technical depth with business acumen
  • Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field (preferred)
  • Deep experience with Large Language Models (LLMs), Small Language Models (SLMs), and generative AI applications (preferred)
  • Expertise in building production AI agent systems including multi-agent architectures, memory systems, planning algorithms, tool use and agent communication protocols (preferred)
  • Experience with advanced agent frameworks (DSPy, Guidance, LMQL, Outlines) and prompt engineering techniques (few-shot, chain-of-thought, constitutional AI) (preferred)
  • Experience with RAG architectures, vector stores, re-ranking and query optimization (preferred)
  • Expertise in training techniques (supervised fine-tuning, RLHF, DPO, PPO) and parameter-efficient fine-tuning methods (LoRA, QLoRA) (preferred)
  • Knowledge of model optimization techniques (quantization, distillation, pruning) and efficiency methods (flash attention) (preferred)
  • Extensive experience in dataset curation for LLM training and web-scale data processing (Common Crawl, C4) (preferred)
  • Experience with data augmentation, decontamination, and benchmark pollution prevention (preferred)
  • Experience with workflow automation platforms (n8n, Zapier, Make) and enterprise integration patterns (event-driven, saga, CQRS) (preferred)
  • Strong background in data science: statistical analysis, A/B testing, experimentation design, and causal inference (preferred)
  • Experience with data mesh architectures, data quality frameworks, data contracts, and SLA management (preferred)
  • Experience with vector databases (Pinecone, Weaviate, Qdrant, Milvus, ChromaDB, FAISS) and embedding systems (preferred)
  • Knowledge of privacy-preserving ML techniques: differential privacy, federated learning, secure multi-party computation (preferred)
  • Background in specific AI domains: NLP, Computer Vision, Recommendation Systems, or Reinforcement Learning (preferred)
  • Experience with LLM evaluation frameworks and popular LLM frameworks (Hugging Face, vLLM, TGI, Ollama, LiteLLM) (preferred)
  • Experience with dataset processing tools (Datasets library, Apache Beam, Spark NLP) (preferred)
  • Publications or contributions to open-source ML projects (preferred)
  • Experience in high-growth technology companies or AI-first organizations (preferred)
  • Knowledge of AI safety, ethics, and responsible AI practices (preferred)
  • Experience with multi-modal and vision-language models (preferred)

Benefits

  • Opportunity to work on cutting-edge AI technology with real-world impact
  • Competitive compensation package including equity
  • Access to state-of-the-art computing resources and research tools
  • Budget for conferences, training, and professional development
  • Collaborative environment with talented engineers and researchers
  • Flexible work arrangements and comprehensive benefits
  • 5 global recharge days, in addition to standard holidays, and a hybrid, flexible approach to work
  • Parental leave for all parents, an annual wellness stipend and volunteer days
  • Opportunities throughout the year to learn and celebrate diversity
  • Access to leading AI tools and foundation models, with the freedom to experiment and find creative ways to be more effective in your role

Job title

Senior Engineering Manager – AI

Job type

Full Time

Experience level

Senior

Salary

Not specified

Degree requirement

No Education Requirement

Tech skills

AirflowApacheAWSAzureCloudDockerETLFlashGoogle Cloud PlatformJavaKubernetesPythonPyTorchRayScalaSparkTensorflow

Location requirements

HybridChennaiIndia

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