AI/ML Engineer_

Primary Responsibilities:

• Research & Development
  • Lead applied research in LLMs, generative AI, and multimodal models
  • Evaluate and experiment with state-of-the-art architectures (e.g., Transformers, Diffusion Models, Retrieval-Augmented Generation).
  • Publish internal whitepapers and contribute to external conferences where applicable.
    Engineering & Implementation
  • Design and implement scalable AIML pipelines using frameworks like PyTorch, TensorFlow, Hugging Face, and MLflow.
  • Collaborate with engineering teams to deploy models into production using MLOps best practices (CI/CD, model versioning, monitoring)
• Tooling & Infrastructure
  • Evaluate and integrate advanced AIML tools such as GitHub Copilot, Windsurf, Vertex AI, Azure OpenAI, and Hugging Face Transformers.
  • Work with cloud platforms (AWS, Azure, GCP) to ensure scalable and secure model deployment
  • Architecture & Strategy
  • Architect end-to-end AIML systems including data ingestion, model training, inference, and feedback loops
  • Partner with enterprise architects and product leaders to align AIML capabilities with business goals 3
• Mentorship & Collaboration
  • Mentor junior engineers and researchers
  • Collaborate with cross-functional teams including data scientists, software engineers, and business stakeholders
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• Bachelors Degree in Computer Science, Machine Learning, or related field
• 4+ years of experience in AIML engineering and research
• Experience with experiment tracking tools (e.g., Weights & Biases, MLflow)
• Hands-on experience with MLOps, model deployment, and monitoring
• Proven expertise in LLMs, generative AI, and deep learning
• Solid programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, Keras)
• Familiarity with AIML governance, ethics, and responsible AI practices