Machine Learning Engineer

We are seeking a Senior Machine Learning Engineer with strong expertise in Large Language Models (LLMs), Deep Learning, NLP, and MLOps. The successful candidate will design, develop, and deploy scalable ML solutions, lead and mentor engineers, and collaborate with cross-functional teams to deliver production-ready AI systems that meet security, privacy, and regulatory requirements.

• Design, develop, and deploy scalable and reliable machine learning solutions with a focus on LLMs and NLP.
  

• Work hands-on with LLM APIs, including:
  

  • Prompt engineering
    

  • LLM agents
    

  • Response handling, optimization, and evaluation
    

• Apply strong software engineering best practices, including design patterns, testing strategies, and deployment standards.
  

• Lead and mentor machine learning engineers and developers, providing technical guidance and code reviews.
  

• Collaborate with product, engineering, DevOps, and compliance teams to deliver end-to-end ML solutions.
  

• Ensure ML systems meet risk, privacy, and regulatory compliance requirements.
  

• Contribute to DevOps and MLOps practices, including CI/CD pipelines and production deployments.
  

• Improve model performance, reliability, and observability in production environments.
  

  
  

• 3+ years of hands-on experience in machine learning development.
  

• Strong experience in Deep Learning and Natural Language Processing (NLP).
  

• Hands-on experience working with Large Language Model (LLM) APIs.
  

• Proven experience leading or mentoring ML engineers or developers.
  

• Strong programming skills in Python, Java, or C/C++.
  

• Hands-on experience with cloud platforms: AWS, GCP, or Azure.
  

• Solid understanding of MLOps tools and workflows.
  

• Experience with DevOps practices, including:
  

  • CI/CD pipelines
    

  • Docker-based containerization
    

  • Kubernetes orchestration
    

• Experience with ML frameworks such as PyTorch, TensorFlow, or equivalent.
  

• Strong communication, leadership, and collaboration skills.
  

  
  

• Experience with one or more orchestration platforms:
  

  • Apache Airflow
    

  • Kubeflow
    

  • Dagster
    

  • Flyte
    

  • Metaflow
    

  
  

• Familiarity with Snowflake, Airflow, or similar data warehousing and orchestration tools.
  

• Strong understanding of:
  

  • CI/CD principles
    

  • Version control systems (Git)
    

  • Production ML deployment best practices
    

  
  

• Strong problem-solving and analytical skills
  

• Ability to design production-grade ML systems
  

• Leadership and mentoring capability
  

• Collaborative mindset across engineering and business teams
  

• Focus on scalability, reliability, and compliance