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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, common mid-level ML role, metro location, and broad skillset increase candidate competition.
Core ML engineering skills are transferable, but payments and enterprise GenAI experience preference raises domain specificity.
Specific 3–4 year requirement plus mandatory Databricks, PyTorch, MLflow, AWS, and MLOps skills.
Job Description
Structured overview of role & requirementsAbout This Role
Build and productionize enterprise-grade AI and Generative AI solutions including fine-tuning foundation models and developing end-to-end ML pipelines with Databricks and AWS.
Design, develop, test, and maintain scalable AI/ML applications, APIs, reusable components, and implement CI/CD, automated testing, and production support for ML workflows.
Collaborate across data science, engineering, product, security, privacy, and governance teams to deliver reliable, secure, and responsible AI solutions.
Minimum Requirements
3–4 years of experience in AI/ML engineering, software engineering, data science, or related field.
Strong skills in Python, SQL, object-oriented programming, API development, and software design fundamentals.
Hands-on experience with Databricks, Spark/PySpark, MLflow, AWS cloud services, PyTorch, Hugging Face, FastAPI/Flask, Docker, Kubernetes, and CI/CD.
Bachelor’s or Master’s degree in Computer Science, Information Technology, or related STEM field.
Ideal Candidate Profile
Experienced in combining software engineering with ML model fine-tuning and MLOps in cloud environments (AWS, Databricks).
Familiarity with foundation models and Generative AI workflows including RAG, embeddings, vector search, and AI agents preferably in enterprise or financial services contexts.
Proficient in production ML operations focusing on model performance, scalability, security, and responsible AI governance.