Machine Learning Engineer

Machine Learning Engineer

Key Responsibilities:-

• Design, develop, test, and deploy scalable machine learning systems with a focus on accuracy, performance, and efficiency
• Build and optimize ML models using traditional statistical methods, deep learning, and Large Language Models (LLMs)
• Prepare, clean, and transform large datasets for training and evaluation
• Implement and optimize machine learning algorithms and statistical models
• Integrate ML models into existing applications, systems, and workflows
• Deploy models into production and continuously monitor performance
• Collaborate closely with data scientists, software engineers, and business stakeholders
• Identify opportunities for continuous improvement in models, pipelines, and systems

Required Skills & Expertise:-

Programming & Engineering

• Strong software engineering fundamentals (version control, testing, CI/CD practices)

Data Engineering

• Hands-on experience with data pipelines, data cleaning, and feature engineering
• Proficiency in SQL for data analysis and manipulation
• Experience troubleshooting with tools such as Kafka and Chaossearch logs
• Exposure to ScyllaDB (BigTable-like), OpenSearch, and Neo4j (graph databases)

MLOps & Deployment

• Experience deploying and maintaining ML models in production environments
• Knowledge of model monitoring, performance evaluation, and retraining strategies

AWS & Cloud Requirements

• Amazon SageMaker:

  Deep understanding of building, training, and deploying ML models; experience with SageMaker pipelines and improving existing workflows

• AWS Cloud Services:

  Hands-on experience with S3, EC2, Lambda in ML workflows

• AWS Data Services:

  Redshift, Glue

• Containerization & Orchestration:

  Docker and Kubernetes with AWS services such as EKS and ECS

Skills & Competencies:-

• 7–9 years

  of hands-on experience in

  Machine Learning Engineering

  and production-grade ML system
• Experience with

  Deep Learning frameworks

  and

  Large Language Models (LLMs)

• Hands-on expertise with

  Amazon SageMaker

  for model building, training, deployment, and pipeline optimization
• Solid understanding of

  MLOps

  practices including model deployment, monitoring, and performance evaluation
• Strong programming skills with adherence to

  software engineering best practices

  (Git, testing, CI/CD)
• Experience in

  data engineering

  , including data pipelines, feature engineering, and data preprocessing
• Proficient in

  SQL

  for data analysis and data manipulationdeep
• Hands-on experience with

  Kafka

  and log analysis tools such as

  Chaossearch

• Working knowledge of

  ScyllaDB

  ,

  OpenSearch

  , and

  Neo4j

  (graph databases)
• Experience with

  AWS Cloud Services

  including

  S3

  ,

  EC2

  ,

  Lambda

• Exposure to

  AWS Data Services

  such as

  Redshift

  and

  Glue

• Experience with

  containerization

  using

  Docker

• Working knowledge of

  Kubernetes

  and AWS orchestration services (

  EKS

  ,

  ECS

  )