# Principal Machine Learning Engineer

**Company:** [Ema](https://hotfix.jobs/companies/ema)
**Location:** Bengaluru, India
**Role:** ML Engineering
**Experience:** 10+ years
**Skills:** Python, C++, Java, TensorFlow, PyTorch, Machine Learning, LLMs, Retrieval-Augmented Generation, Agentic Systems, Knowledge Retrieval, Search, Distributed Training, Quantization, Pruning, Model Monitoring
**Posted:** 2025-10-06

> Leads the architecture, roadmap, and production execution of GenAI and agentic machine-learning systems at enterprise scale. The role requires 10–12+ years of applied ML experience, expertise in retrieval and large-scale systems, and the ability to mentor senior technical teams.

## Job Description

## Responsibilities
- Lead the technical direction of GenAI and agentic ML systems powering enterprise-grade AI agents, including reasoning, retrieval, tool use, and SaaS integrations.
- Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval-augmented generation (RAG), agent orchestration, and evaluation.
- Define and own the multi-year ML roadmap for GenAI infrastructure, including agent frameworks, RAG systems, evaluation loops, and MCP, browser, and vision integrations.
- Identify, 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, from prompt design through orchestration and execution.
- Drive observability, reproducibility, versioning, testing, and bias-aware development across ML and agentic systems.
- Mentor senior engineers and researchers while fostering scientific rigor, experimentation, and system-level thinking.
- Collaborate with product, infrastructure, and research teams to align ML innovation with enterprise needs, secure integrations, privacy-aware deployments, and scalable use cases.
- Guide data strategy, including retrieval indices, embeddings, structured and unstructured corpora, and feedback loops.
- Ensure ML agents and RAG pipelines scale across billions of knowledge objects, diverse APIs, and real-time enterprise contexts.

## Requirements
- Bachelor's, master's, or PhD in Computer Science, Machine Learning, Statistics, or a related field.
- 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.
- 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, model training, serving, monitoring, and maintenance.
- Experience designing monitoring, diagnostics, logging, and model-versioning systems.
- Deep knowledge of distributed training and inference optimization techniques such as quantization, pruning, and batching.
- Excellent communication skills and ability to explain complex systems and trade-offs to technical and non-technical stakeholders.
- Experience mentoring senior engineers and leading technical discussions across organizations.

## Compensation & Benefits
- Compensation is determined by location, level, job-related knowledge, skills, and experience.
- Certain roles may be eligible for variable compensation, equity, and benefits.

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