Key Responsibilities
ML backend and Orchestration
Hands on development of ML Systems backend infrastructure, messaging and integration with interfacing systems
Design, build, and maintain APIs, Microservices, and systems to serve data science models.
Ensure ML Infrastructure systems are scalable, reliable, and secure
Database Management
Work with SQLAlchemy to define, maintain, and query the database schema for an application.
Work with raw SQL to run advanced queries for things like alerting and reporting.
ML Ops Implementation
Develop and maintain CI/CD pipelines for data science models and services
Automate and track data science workflows with tools like MLflow
Infrastructure:
Manage and optimize Kubernetes cluster via Openshift.
Implement and manage various infrastructure components such as PostgreSQL, Kafka, S3.
Knowledge of running workloads in AWS or GCP will be plus.
Collaboration:
Work with data scientists to understand model requirements and ensure successful deployment into production systems
Collaborate with DevOps and infrastructure teams to improve scalability and reliability
Performance & Optimization:
Optimize backend services and data science model inference for latency and throughput
Troubleshoot issues in production environments, ensuring high availability of services
Skills & Qualifications
Proficiency in Python
Hands on experience working with a Python web framework such as FastAPI, Flask, Django, etc
Peripheral knowledge in Machine Learning and Data Science
Working knowledge in MLOps principals such as experiment tracking, model serving, model orchestration, etc.
Hands on experience with designing DB driven applications and using an ORM such as SQLAlchemy.
Hands on experience with deploying and maintaining production applications
Hands on experience with managing and debugging the necessary infrastructure to support a full stack application
Hands on experience with Kubernetes
Hands on experience working with stream processing tools like Kafka or Apache Spark
Bachelors degree or equivalent experience in Computer Science or a related field.
Ideal skills
Hands on experience working with a Python orchestrator (Dagster, Airflow, Prefect, etc)
Hands on experience working with various MLOps tools such as MLFlow, Kedro, etc
Hands on experience working with LLMs and related technologies such as Vector DBs, Agents, etc.
ML Solutions team is seeking a skilled and innovative ML Systems Engineer with ML Ops experience to join our team. In this role, you will be responsible for building and maintaining scalable backend systems, deploying data science (DS) models, and designing robust pipelines that enable seamless integration of DS solutions into our applications. You will work closely with data scientists and front end engineers to help bridge the gap between data science and production-ready systems. If youre passionate about operationalizing data Science models, creating reliable backend services, and improving the lifecycle of data science workflows, this role could be a great fit for you.
Key Responsibilities
ML backend and Orchestration
Hands on development of ML Systems backend infrastructure, messaging and integration with interfacing systems
Design, build, and maintain APIs, Microservices, and systems to serve data science models.
Ensure ML Infrastructure systems are scalable, reliable, and secure
Database Management
Work with SQLAlchemy to define, maintain, and query the database schema for an application.
Work with raw SQL to run advanced queries for things like alerting and reporting.
ML Ops Implementation
Develop and maintain CI/CD pipelines for data science models and services
Automate and track data science workflows with tools like MLflow
Infrastructure:
Manage and optimize Kubernetes cluster via Openshift.
Implement and manage various infrastructure components such as PostgreSQL, Kafka, S3.
Knowledge of running workloads in AWS or GCP will be plus.
Collaboration:
Work with data scientists to understand model requirements and ensure successful deployment into production systems
Collaborate with DevOps and infrastructure teams to improve scalability and reliability
Performance & Optimization:
Optimize backend services and data science model inference for latency and throughput
Troubleshoot issues in production environments, ensuring high availability of services
Skills & Qualifications
Proficiency in Python
Hands on experience working with a Python web framework such as FastAPI, Flask, Django, etc
Peripheral knowledge in Machine Learning and Data Science
Working knowledge in MLOps principals such as experiment tracking, model serving, model orchestration, etc.
Hands on experience with designing DB driven applications and using an ORM such as SQLAlchemy.
Hands on experience with deploying and maintaining production applications
Hands on experience with managing and debugging the necessary infrastructure to support a full stack application
Hands on experience with Kubernetes
Hands on experience working with stream processing tools like Kafka or Apache Spark
Bachelors degree or equivalent experience in Computer Science or a related field.
Ideal skills
Hands on experience working with a Python orchestrator (Dagster, Airflow, Prefect, etc)
Hands on experience working with various MLOps tools such as MLFlow, Kedro, etc
Hands on experience working with LLMs and related technologies such as Vector DBs, Agents, etc.
Key job details
About Virtusa
Teamwork, quality of life, professional and personal development: values that Virtusa is proud to embody. When you join us, you join a team of 27,000 people globally that cares about your growth — one that seeks to provide you with exciting projects, opportunities and work with state of the art technologies throughout your career with us.
Great minds, great potential: it all comes together at Virtusa. We value collaboration and the team environment of our company, and seek to provide great minds with a dynamic place to nurture new ideas and foster excellence.
Virtusa is an Equal Opportunity Employer. All applicants will receive fair and impartial treatment without regard to race, color, religion, sex, national origin, ancestry, age, legally protected physical or mental disability, protected veteran status, status in the U.S. uniformed services, sexual orientation, gender identity or expression, marital status, genetic information or on any other basis which is protected under applicable federal, state or local law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government-issued ID during each interview. All candidates must be authorized to work in the USA.
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