GEN AI Full-time

Description

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.