Senior Software Engineer - Machine Learning - Janea Systems

JJanea Systems (USA) is a dynamic team of the best & brightest software engineering specialists and solutions innovators, from around the world. From kernel to cloud, we provide high-impact software development services to Fortune 500 companies.
We are urgently looking an exceptionally talented Senior Machine Learning Engineer to join our rapidly growing consulting team. In this role, you will have the opportunity to work at the cutting edge of the software industry and help work on the client's internal AI/ML practice, as well as utilize your LLM, Data Engineering, ML, and ML Ops skills while working with a team of highly skilled professionals in the AI/ML domain.

• To be considered for this position, you must have the following qualifications:

  • Bachelor's or Master’s degree in Computer Science or a related field
  • 4+ years of experience as a Software Engineer, Platform Engineer, ML Engineer, Data Scientist, AI Engineer, or Data Engineer
  • Flexibility in experience with different programming languages and willingness to adjust to project needs
  • Strong knowledge of Python
  • Knowledge of machine learning algorithms, data pre-processing methods, and ML frameworks (such as PyTorch, TensorFlow, Keras)
  • Experience with containers and Kubernetes in cloud environments (AWS, MS Azure, or GCP)
  • Familiarity with data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo)
  • Understanding of software testing, benchmarking, and continuous integration principles
  • Ability to translate business needs into technical requirements
  • Excellent communication and problem-solving skills, with the ability to break down complex challenges and develop innovative solutions
  • Being self-motivated and adaptable, with the ability to work effectively in fast-paced, dynamic environment
    
    
  Ideal candidates will also have:
  • Familiarity with agent frameworks (such as Langchain, Langgraph, IllamaIndex)
  • Familiarity with developing RAG systems
  • Experience with Natural Language Processing (NLP)
  • Familiarity with monitoring tools (such as DataDog or Langfuse)
  • Any associate cloud certification (AWS preferred)
  Responsibilities:
  • Design scalable data pipelines and infrastructure for enterprise ML systems
  • Implement ML models and systems into production
  • Collaborate with data scientists and software engineers
  • Deploy scalable tools and services for machine learning training and inference
  • Evaluate new technologies to improve ML system performance and reliability
  • Apply software engineering best practices, including CI/CD, to ML development
  • Facilitate the development and deployment of ML proof-of-concepts
  • Review, refactor, optimize, containerize, deploy, version, and monitor ML models
  • Implement monitoring and alerting solutions to ensure the reliability and performance of machine learning systems
  • Optimize and automate the machine learning deployment process to ensure efficiency and reproducibility
  • Collaborate with cross-functional teams to troubleshoot and resolve issues related to machine learning deployments
  • Stay updated with industry trends and apply knowledge to drive innovation
  • Promote industry best practices and enhance team expertise
    
    
  Why join Janea? Because world-class talent deserves world-class opportunities. What we offer:
  • Competitive compensation with benefits, paid vacation, and sick leave.
  • The opportunity to work with a globally diverse team of top engineering talent on the industry’s toughest engineering challenges.
  • Ultra-flexible working conditions – we provide a generous office equipment allowance so you can work from home, we can also provide you with a desk at an office/coworking facility near you, or use both. No business travel necessary.
  • An enjoyable, start-up work environment, with excellent opportunities for professional growth and development.
  • Flexible working hours – as a remote-first company, our focus has always been on getting the job done well, not when or where it gets done.
   

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