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Principal Machine Learning Engineer

Ema· Engineering
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India - BengaluruFull-timePosted 11mo ago

About the role

A leadership role shaping Ema's AI/ML roadmap and building large-scale ML systems, spanning architecture, execution, and org-building for enterprise-grade agentic AI capabilities.

Responsibilities

  • Lead the technical direction of GenAI and agentic ML systems that power enterprise-grade AI agents — spanning reasoning, retrieval, tool use, and integrations across various SaaS products.
  • Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval (RAG), agent orchestration, and evaluation — ensuring robustness, latency efficiency, and continuous learning.
  • Define and own the multi-year ML roadmap for GenAI infrastructure — including agent frameworks, RAG systems, world-class evaluation loops, and integration with MCP, browser, and vision pipelines.
  • Identify and integrate cutting-edge ML methods / research (deep learning, large models, recommender systems, LLMs, etc.) into Ema’s products or infrastructure.
  • Research, prototype, and integrate cutting-edge ML and LLM advancements (reasoning, memory architectures, multi-modal perception, long-context models, autonomous agents) into the platform.
  • Optimize trade-offs between accuracy, latency, cost, interpretability, and real-world reliability across the agent lifecycle — from prompt design to orchestration and execution.
  • Champion engineering excellence — drive observability, reproducibility, versioning, testing, and bias-aware development across ML and agentic systems.
  • Mentor and elevate senior engineers and researchers, fostering a culture of scientific rigor, experimentation, and system-level thinking.
  • Collaborate cross-functionally with product, infra, and research teams to align ML innovation with enterprise needs — enabling secure integrations, privacy-aware deployments, and scalable use cases.
  • Influence data strategy — guide how retrieval indices, embeddings, structured/unstructured corpora, and feedback loops evolve to improve grounding, factuality, and reasoning depth.
  • Drive system scalability and performance — ensuring ML agents and RAG pipelines can operate across billions of knowledge objects, diverse APIs, and real-time enterprise contexts.

Qualifications

  • Bachelor’s or Master’s (or PhD) degree in Computer Science, Machine Learning, Statistics, or a related field.
  • A strong track record (usually 10-12+ years) of applied experience with ML techniques, especially in large-scale settings.
  • Experience building production ML systems that operate at scale (latency / throughput / cost constraints).
  • Experience in Knowledge retrieval and Search space.
  • Exposure in building Agentic Systems and Frameworks.
  • Proficiency in relevant programming languages (e.g. Python, C++, Java) and ML frameworks (TensorFlow, PyTorch, etc.).
  • Strong understanding of the full ML lifecycle: data pipelines, feature engineering, model training, serving, monitoring, maintenance.
  • Experience designing systems for monitoring, diagnostics, logging, model versioning, etc.
  • Deep knowledge of computational trade-offs: distributed training, inference, optimizations (e.g. quantization, pruning, batching).
  • Excellent communication skills; ability to present complex systems / trade-offs to technical and non-technical stakeholders.
  • Experience mentoring senior engineers; ability to lead technical discussions and influence across orgs.

Skills

Required:GenAI and agentic ML systemsProduction ML systems at scaleKnowledge retrieval / searchAgentic systems / frameworksProgramming languages
Last updated Oct 8, 2026. Interested? Apply on the company site.
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