ML Ops Engineer

MLOps Engineer (ML, Speech, NLP & Multimodal Expertise)

We are looking to hire a MLOps Engineer with strong expertise in machine learning, speech and

language processing, and multimodal systems. This role is essential to driving our product

roadmap forward, particularly in deploying, testing, evaluating and monitoring our core machine

learning systems and developing next-generation speech technologies.

The ideal candidate will be capable of working independently while effectively collaborating

with cross-functional teams. In addition to deep technical knowledge, we are looking for

someone who is curious, experimental, and communicative.

Key Responsibilities

Essential

• Design and maintain CI/CD pipelines for automated model training, testing, and

deployment.

• Build container orchestration solutions (Docker, Kubernetes) for model serving at scale.

• Implement deployment strategies (blue-green, canary, A/B testing) for safe model

rollouts.

• Develop Infrastructure as Code (Terraform, CloudFormation) for reproducible ML

environments.

• Optimize model serving infrastructure for latency, throughput, and cost efficiency.

• Manage model versioning, registry, and artifact storage systems.

• Build real-time monitoring dashboards for model performance, latency, and resource

utilization.

• Implement automated alerting systems for model degradation and anomaly detection.

• Design feature drift detection and data quality monitoring for production traffic.

• Create feedback loops to capture user interactions and model effectiveness.

• Develop automated retraining triggers based on performance degradation signals.

• Track business metrics and ROI analysis for model deployments.

• Create interactive dashboards and visualization tools for model performance analysis.

• Build specialized inference pipelines for speech-to-text and text-to-speech models.

• Optimize speech model performance for real-time and batch processing scenarios.

• Design evaluation frameworks specific to speech quality metrics (WER, latency,

naturalness).

• Handle multi-modal data pipelines combining audio, text, and metadata.

• Implement feature drift detection and data quality monitoring.

• Design feedback loops to capture user interactions and model effectiveness.

• Create automated retraining pipelines based on performance degradation signals.

• Develop business metrics and ROI analysis for model deployments.

• Implement experiment tracking systems (MLflow, Weights & Biases) for reproducibility.

• Design hyperparameter optimization frameworks for efficient model tuning.

• Conduct statistical analysis of training dynamics and convergence patterns.

• Create automated model selection pipelines based on multiple evaluation criteria.

• Develop cost-benefit analyses for different training configurations and architectures.

Additional Responsibilities

• Implement automated evaluation pipelines that scale across multiple models and

benchmarks.

• Design comprehensive test suites with statistical significance testing for model

comparisons.

• Develop fairness metrics and bias detection systems for speech models across

demographics.

• Perform statistical analysis of training datasets to identify quality issues and coverage

gaps.

• Create interactive dashboards and visualization tools for model performance analysis.

• Build A/B testing frameworks for comparing model versions in production.

• Build and maintain ETL pipelines using SQL, Azure, GCP, and AWS technologies.

• Design data ingestion systems for massive-scale speech and text corpora.

• Implement data validation frameworks and automated quality checks.

• Create sampling strategies for balanced and representative training datasets.

• Develop data preprocessing and cleaning pipelines for audio and text.

Required Skills and Qualifications

Essential

Programming & Software Engineering

•
Python (Expert Level)
: Advanced proficiency in scientific computing stack (NumPy,

Pandas, SciPy, Scikit-learn).

•
Version Control
: Git workflows, collaborative development, and code review processes.

•
Software Engineering Practices
: Testing frameworks, CI/CD pipelines, and production-

quality code development.

Machine Learning and Language Model Expertise

•
Traditional Machine Learning and Deep Learning Knowledge:
Proficiency in

classical ML algorithms (Naive Bayes, SVM, Random Forest, etc.) and Deep Learning

architectures.

•
Understanding of Transformer Architecture:
Attention mechanisms, positional

encoding, and scaling laws.

•
Training Pipeline Knowledge:
Data preprocessing for large corpora, tokenization

strategies, and distributed training concepts.

•
Evaluation Frameworks:
Experience with standard NLP benchmarks (GLUE,

SuperGLUE, etc.) and custom evaluation design.

•
Fine-tuning Techniques:
Understanding of PEFT methods, instruction tuning, and

alignment techniques.

•
Model Deployment:
Knowledge of model optimization, quantization, and serving

infrastructure for large models.

Additional skills

Machine Learning & Deep Learning

•
Framework Proficiency
: Scikit-learn, XGBoost, PyTorch (preferred) or TensorFlow for

model implementation and experimentation.
•
MLOps Expertise
: Model versioning, experiment tracking, model monitoring (MLflow,

Weights & Biases), data monitoring, observability and validation (Great Expectations,

Prometheus, Grafana), and automated ML pipelines (GitHub CI/CD, Jenkins, CircleCI,

GitLab etc.).

•
Statistical Modeling
: Hypothesis testing, experimental design, causal inference, and

Bayesian statistics.

•
Model Evaluation
: Cross-validation strategies, bias-variance analysis, and performance

metric design.

•
Feature Engineering
: Advanced techniques for text, time-series, and multimodal data.

Data Engineering & Infrastructure

•
Speech Processing Libraries:
Librosa, Torchaudio, SpeechBrain, Kaldi, Espnet

•
Feature Stores and Data Versioning:
Feast, Tecton, DVC

•
Big Data Technologies:
Spark (PySpark), Hadoop ecosystem, and distributed computing

frameworks (DDP, TP, FSDP).

•
Cloud Platforms:
AWS (SageMaker, Bedrock, S3, EMR), GCP (Vertex AI, BigQuery),

or Azure ML.

•
Database Systems:
NoSQL databases (MongoDB, Elasticsearch), graph databases

(Neo4j), and vector databases (Pinecone, Milvus, ChromaDB, FAISS etc.).

•
Data Pipeline Tools:
Airflow, Prefect, or similar orchestration frameworks.

•
Containerization:
Docker, Kubernetes for scalable model deployment

•
Model Serving Frameworks:
TorchServe, TensorFlow Serving, Triton

•
Infrastructure as Code Tools
: Terraform, CloudFormation

Collaboration & Adaptability

• Strong communication skills are a must

• Self-reliant but knows when to ask for help

• Comfortable working in an environment where conventional development practices may

not always apply:
• PBIs (Product Backlog Items) may not be highly detailed

• Experimentation will be necessary

• Ability to identify what's important in completing a task or partial task and

explain/justify their approach

• Can effectively communicate ideas and strategies

• Proactive and takes initiative rather than waiting for PBIs to be assigned when

circumstances call for it

• Strong interest in AI and its possibilities, a genuine passion for certain areas can provide

that extra spark

• Curious and open to experimenting with technologies or languages outside their comfort

zone

Mindset & Work Approach

• Takes ownership when things don't go as planned

• Capable of working from high-level explanations and general guidance on

implementations and final outcomes

• Continuous, clear communication is crucial, detailed step-by-step instructions won't

always be available

• Self-starter, self-motivated, and proactive in problem-solving

• Enjoys exploring and testing different approaches, even in unfamiliar programming

languages

Where Your Career is Going:

At TransPerfect, there are a lot of growth opportunities
.
All departments offer career growth and development that can combine your skills, interest and experience. We encourage our employees to have a continuous dialogue with management about growth opportunities throughout your tenure with the company.

End your job search and find your career at TransPerfect #careersNOTjobs.

Why TransPerfect:

For more than 25 years, we have honed a culture where all kinds of ideas are shared and new ventures are not only welcomed, but also encouraged. In this fast-paced environment, employees are intellectually stimulated so they can grow alongside the organization. From Intern to President, we believe that every single employee should have a voice and contribute to the amazing services we offer our clients.

We also offer a comprehensive benefits package including medical, dental, and vision insurance, 401k matching, membership to child-care providers, and other TransPerks. You even get your birthday off because let's face it, we're stoked that you were born.

TransPerfect provides equal employment opportunity to all individuals regardless of their race, color, creed, religion, gender, age, sexual orientation, national origin, disability, veteran status, or any other characteristic protected by state, federal, or local law. TransPerfect is committed to all recruitment processes and workplace free from harassment, sexual harassment & discrimination.

For more information on the TransPerfect Family of Companies, please visit our website