Principal Machine Learning Engineer Job at Ema (Bengaluru)

Principal Machine Learning Engineer

Ema

Location:
India , Bengaluru

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Not provided

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Job Description:

This is a high-leverage leadership role that spans architecture, execution, and org-building, and will shape the direction of our AI / ML initiatives at Ema. We are seeking an AI / ML technical leader who can take a vision and build it. As a Principal ML Engineer at Ema, you will be a senior technical leader responsible for shaping the machine learning roadmap, architecting large-scale ML systems, driving innovation, and ensuring our mixture of expert models (LLM + SLM + Custom Model) is accurate and performant at scale. You will collaborate across teams (research, product, infra, data, etc.), mentor senior engineers, and influence strategy and execution at company-wide levels.

Job Responsibility:

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

Requirements:

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

Additional Information:

Job Posted:
February 21, 2026

Employment Type:

Fulltime

Work Type:

Hybrid work

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