12 months ago
Bengaluru, IndiaStaff+
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.
Categories
About Ema
Ema builds an agentic AI platform that delivers AI employees to automate HR, IT, and Finance workflows across existing enterprise systems. It sells to large organizations via enterprise software and implementation services, offering on-prem and air-gapped deployments and 1,000+ prebuilt integrations. Founded in 2023, it has deployments with Wipro and Hitachi.