Software Engineer-ML Engineer II

• Translate business requirements into scalable and well-documented ML pipelines and AI solutions using Databricks, Azure AI, and Snowflake.
  

• Collaborate cross-functionally with data scientists, product managers, and engineering teams to design and deploy ML models that meet business needs in terms of performance, reliability, and scalability.
  

• Design and implement MLOps frameworks for model lifecycle management, including CI/CD, MLflow, ensuring reproducibility and traceability.
  

• Develop and maintain data pipelines for feature engineering, model training, and inference using tools like DBT and Spark.
  

• Support production rollouts of ML models and troubleshoot post-deployment issues such as model drift, data quality, and latency bottlenecks.
  

• Contribute to AI governance by aligning with enterprise standards for model explainability, fairness, and security (e.g., prompt injection, data leakage mitigation).
  

• Stay current with GenAI and LLM advancements, including frameworks like LangChain, LlamaIndex, and Gemini, and apply them to enterprise use cases.
  

Basic Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.
  

• 2 to 5 years of experience in AI/ML engineering, with hands-on exposure to enterprise-grade ML pipelines and AI systems.
  

• Proficiency in Python and familiarity with ML/AI libraries such as PyTorch, TensorFlow, Scikit-learn, and LangChain.
  

• Experience with cloud platforms, especially Azure AI, Databricks, and Snowflake, including model deployment and data orchestration.
  

• Understanding of MLOps/LLMOps frameworks, including CI/CD, MLflow, ONNX, or DVC, with 2+ years of experience in model lifecycle management.
  

• Familiarity with modern UI and API technologies, such as FastAPI, Docker, and AKS, for building and deploying AI services.
  

Preferred Qualifications

• Experience in Test Driven Development and model evaluation strategies, including offline and online evaluation pipelines.
  

• Prior work experience in an agile, cross-functional team, collaborating with data scientists, engineers, and product managers.
  

• Awareness and application of continuous integration (CI) and Infrastructure as Code (IaC) principles for AI/ML workflows.
  

• Ability to break down complex AI problems, estimate development effort, and deliver scalable solutions.
  

• Up-to-date knowledge of GenAI and LLM trends, including frameworks like Gemini, LlamaIndex, and LangGraph, and their enterprise applications.
  

• Understanding of AI governance, including model explainability, fairness, and security (e.g., prompt injection, data leakage mitigation).