11 months ago
Responsibilities
- Lead the technical direction of GenAI and agentic ML systems powering enterprise AI agents.
- Architect, design, and implement scalable production pipelines for training, fine-tuning, retrieval, agent orchestration, and evaluation.
- Own the multi-year roadmap for GenAI infrastructure, including agent frameworks, retrieval-augmented generation systems, evaluation loops, and integrations with MCP, browser, and vision pipelines.
- Research, prototype, and integrate advances in deep learning, large models, recommender systems, LLMs, reasoning, memory architectures, multimodal perception, long-context models, and autonomous agents.
- Optimize accuracy, latency, cost, interpretability, and reliability across the agent lifecycle.
- Drive observability, reproducibility, versioning, testing, and bias-aware development across ML and agentic systems.
- Mentor and elevate senior engineers and researchers while fostering scientific rigor and system-level thinking.
- Collaborate with product, infrastructure, research, and data teams to align ML innovation with enterprise needs.
- Guide retrieval indices, embeddings, corpora, and feedback loops to improve grounding, factuality, and reasoning depth.
- Ensure ML agents and retrieval pipelines scale across billions of knowledge objects, diverse APIs, and real-time enterprise contexts.
Requirements
- Bachelor’s, Master’s, or PhD degree in Computer Science, Machine Learning, Statistics, or a related field.
- Usually 10–12+ years of applied machine learning experience, especially in large-scale settings.
- Experience building production ML systems under latency, throughput, and cost constraints.
- Experience with knowledge retrieval and search, and exposure to agentic systems and frameworks.
- Proficiency in Python, C++, or Java and ML frameworks such as TensorFlow and PyTorch.
- Strong understanding of the full ML lifecycle, including data pipelines, feature engineering, training, serving, monitoring, and maintenance.
- Experience designing monitoring, diagnostics, logging, and model-versioning systems.
- Deep knowledge of distributed training, inference optimization, quantization, pruning, and batching.
- Experience mentoring senior engineers and influencing technical discussions across organizations.
- Excellent communication skills for presenting complex systems and trade-offs to technical and non-technical stakeholders.
Benefits
- Hybrid work arrangement with three days per week in the office.
- Opportunity to work on agentic AI systems and mission-critical enterprise ML platforms.
- Collaboration with engineers, product thinkers, and AI researchers.
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About Ema
Ema is a suite of AI employees for HR, IT and Finance. She doesn't just automate workflows — she redesigns the work around your outcome and executes it across every enterprise system. Proven at scale across the world's largest enterprises.