MLOps Engineer

MLOps Engineer

Location : Hyderabad / Bangalore, India

Type : Full-Time | Immediate Joining Preferred

CTC : Competitive

About YAL.ai

YAL.ai (Your Alternative Life) is reimagining the way people connect, communicate, and discover in a digital-first world. Our platform brings together instant messaging, dynamic communities, and real-time voice and video, all powered by advanced artificial intelligence. From multilingual speech intelligence and speech-to-speech systems to fraud detection and personalized recommendations, every layer of YAL.ai is designed with security, privacy, and intelligence at its core.

Built on a Zero Trust Architecture, YAL.ai ensures that every interaction is verified and safeguarded. Unlike conventional platforms, we place privacy and user trust at the forefront while still pushing the boundaries of what AI can deliver in real time. With our vision, “Where AI Meets Integrity,” we are building an ecosystem that is fast, safe, multilingual, and personalized creating meaningful and impactful connections for millions of users worldwide.

Role Overview

We are seeking an MLOps Engineer to design, automate, and scale the machine learning infrastructure that powers YAL.ai’s core AI systems. This role sits at the intersection of research and production, ensuring that models built by our AI teams seamlessly transition into robust, secure, and highly available deployments across cloud and edge environments.

Key Responsibilities

End-to-End ML Deployment

Design, build & maintain

production-grade pipelines

for training, deploying, and monitoring ML models.

Deploy models for

speech-to-text, text-to-speech, NLP, and LLM-powered conversational systems

in real-time.

Optimize inference latency for

low-latency streaming systems .

Real-Time & Scalable Infrastructure

Build and maintain

real-time ML services

capable of handling millions of daily requests.

Implement scalable solutions using

Kubernetes, Docker, and cloud-native architectures

(AWS, GCP, or Azure).

Integrate models into

messaging / chat applications

and other conversational platforms.

Automation & CI / CD

Develop

CI / CD pipelines

for continuous training (CT) and continuous deployment (CD) of ML models.

Automate model versioning, packaging, and rollout with tools like

MLflow, Kubeflow, Sagemaker , or similar.

Monitoring & Observability

Create

real-time dashboards

using Prometheus, Grafana, or Datadog for monitoring model health and performance.

Implement

data drift detection, anomaly monitoring , and automated retraining triggers.

Collaboration & Best Practices

Work closely with

data scientists

to productionize research models into stable APIs and services.

Define

best practices for ML pipelines , model governance, and experiment tracking.

Ensure

security and compliance , including safe handling of sensitive data like PII and voice data.

Technical Skills

Technical Expertise :

Strong experience with

Python

and ML deployment frameworks.

Proficiency with

container orchestration : Docker, Kubernetes.

Familiarity with

streaming systems

like Kafka, Redis Streams, or Flink for real-time data.

Hands-on experience deploying

deep learning models

(PyTorch, TensorFlow).

Experience with

GPU / TPU optimization

for real-time inference.

MLOps Tools :

MLflow, Kubeflow, Airflow, or equivalent pipeline orchestration tools.

Model serving frameworks like

TorchServe, TensorFlow Serving, Triton Inference Server , or BentoML.

Cloud ML services (AWS Sagemaker, GCP Vertex AI, Azure ML).

Speech & NLP Focus : Knowledge of

speech processing models

(ASR, TTS, speaker identification).

Experience with

large language models (LLMs)

and

conversational AI architectures .

DevOps & Monitoring :

CI / CD pipelines with GitHub Actions, Jenkins, or GitLab CI.

Observability stack : Prometheus, Grafana, Datadog, ELK.

Soft Skills :

Strong problem-solving and debugging skills.

Excellent communication skills for cross-functional collaboration.

Passion for scalable AI systems and real-time performance.

Qualifications

Bachelor’s or Master’s in Computer Science, Data Engineering, or a related field.

Proven track record of building ML pipelines and deploying models into production.

Experience managing large-scale ML infrastructure with GPUs / TPUs.

Bonus If You Have

Knowledge of on-device / edge AI deployment (TFLite, CoreML, quantization workflows).

Experience with serverless ML serving (Vertex AI, AWS SageMaker, Lambda).

Contributions to open-source MLOps tools or projects.

Experience with multi-model orchestration (fraud detection + speech + recommendations).

How to Apply

Apply directly via LinkedIn or DM us or

Send your CV + infra / work samples (GitHub, architecture diagrams, repos) to hire.ai@yal.chat with Subject : [MLOps Engineer | Your Name]