Experienced Test & Evaluation Engineer

Boeing Boeing · Aerospace · Bangalore, India, India

This role focuses on designing, developing, and deploying machine learning and deep learning models, specifically NLP and generative AI models using architectures like transformers. It involves fine-tuning, prompt-engineering, and distilling LLMs, integrating data into knowledge graphs and vector databases, and selecting appropriate AI/ML frameworks. The role also requires implementing and maintaining data pipelines and monitoring model performance.

What you'd actually do

  1. Design, develop, and deploy machine learning and deep learning models for a variety of business use cases.
  2. Design and develop NLP and generative AI models using architectures like transformers, GPT, BERT, etc.
  3. Fine-tune, prompt-engineer, or distill pre-trained LLMs for domain-specific tasks (e.g., summarization, Q&A, classification).
  4. Collaborate with cross-functional teams to identify business opportunities and translate them into data-driven solutions.
  5. Implement and maintain data pipelines for model training, evaluation, and deployment.

Skills

Required

  • Python
  • data science libraries (e.g., NumPy, pandas, scikit-learn)
  • deep learning frameworks such as TensorFlow or PyTorch
  • generative AI framework (e.g., Hugging Face Transformers, LangChain, LangGraph)
  • machine learning algorithms
  • model evaluation
  • deployment best practices
  • cloud platforms (OpenShift , Kubernetes , Docker)

Nice to have

  • Master’s degree in any engineering domain
  • unstructured data processing
  • Contributions to open-source AI/ML projects or research publications
  • self-starter with a positive attitude, high ethics, and a track record of working independently in developing the analytics solutions
  • working collaboratively with very strong teaming skills
  • willing to work flexible hours
  • Develop and maintain relationships / partnerships with customers, stakeholders, peers, and partners
  • Proactively seek information and direction

What the JD emphasized

  • Experience with generative AI framework
  • Strong understanding of machine learning algorithms, model evaluation, and deployment best practices
  • Knowledge of cloud platforms (OpenShift , Kubernetes , Docker) for model training and deployment

Other signals

  • design, develop, and deploy machine learning and deep learning models
  • design and develop NLP and generative AI models
  • fine-tune, prompt-engineer, or distill pre-trained LLMs
  • integrate external and internal data sources, including unstructured data, into knowledge graphs and vector databases
  • evaluate and select appropriate frameworks and tools for AI/ML projects