Machine Learning Operations Engineer

What role will you play in the team

You will act as a specialized bridge between data science research and enterprise software engineering by owning the deployment, monitoring, and scaling of complex machine learning models. Your mandate involves establishing automated CI/CD pipelines for AI workloads, optimizing Large Language Models and Retrieval-Augmented Generation workflows, and ensuring high availability, ultra-low latency inference, and robust security across all production machine learning environments.

What you will do

You will containerize advanced machine learning models, orchestrate large-scale data processing jobs using Apache Spark and Hadoop, and deploy state-of-the-art AI agents integrated with OpenAI API and Claude API. Your daily tasks include tracking model performance metrics in production, implementing real-time data streaming via Apache Kafka, optimizing ETL pipelines built with Talend and Apache Nifi, and ensuring seamless integration of vector databases.

Key responsibility

• Design, implement, and maintain scalable CI/CD deployment pipelines for machine learning models and artificial intelligence microservices in enterprise production environments.
• Integrate and manage Retrieval-Augmented Generation (RAG) architectures and Large Language Models (LLMs) leveraging OpenAI API, Claude API, and local open-source models.
• Build and optimize distributed data ingestion and processing pipelines utilizing Apache Spark, Hadoop, and Apache Kafka for real-time feature generation.
• Monitor production model performance, track data drift, and execute automated retraining scripts using robust machine learning frameworks like TensorFlow and PyTorch.
  
• Collaborate closely with data science and software development teams to containerize algorithms and implement RESTful APIs and webhooks for model consumption.
• Ensure stringent data governance, security compliance, and latency optimization across all cloud-based and on-premise machine learning inference clusters.

Required Qualification and Skills

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Artificial Intelligence, or a relevant engineering discipline.
• Extensive hands-on experience with Python, advanced SQL, and containerization technologies for deploying machine learning pipelines into production.
• Proven working knowledge of big data technologies including Apache Spark, Hadoop, and real-time streaming platforms like Apache Kafka.
• Demonstrated expertise in deploying Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and working with OpenAI API and Claude API integrations.
• Familiarity with deep learning frameworks such as TensorFlow, PyTorch, and Keras for managing complex neural network inference models.
• Strong understanding of DevOps principles, cloud infrastructure management, automated testing, and API development for AI system deployment.

Benefits Included

Comprehensive medical, life, and accidental insurance policies with cashless hospitalization benefits for employees and their dependents.
Generous professional development stipend for attending global AI/ML summits, acquiring cloud certifications, and purchasing technical equipment.
Structured performance bonuses, employee stock options eligibility, and competitive retirement savings matching schemes.
Hybrid work flexibility supported by high-speed internet allowances, ergonomic home-office setup grants, and modern collaborative office spaces.
Comprehensive wellness programs including mental health support, gym memberships, catered lunches, and regular recreational activities.

A Day in the Life

You start your morning by reviewing automated alerts from production monitoring dashboards, checking the inference latency of deployed Large Language Models. Next, you collaborate with the data science team to containerize a newly trained PyTorch model and integrate it with an existing API webhook. In the afternoon, you optimize an Apache Spark pipeline processing large volumes of streaming data via Apache Kafka, ensuring high data throughput for downstream analytics.