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Mid-level, metro ML role with broad LLM/NLP requirements and common title increases candidate competition.
Core ML/NLP skills are transferable across industries, though B2B SaaS experience is preferred.
Explicit 4–6 years, mandatory ML/NLP, deployment, and Spark/cloud skills create high shortlisting strictness.
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Design, develop, and deploy machine learning and NLP models for customer-facing and business applications, including transformer-based models for classification, ranking, recommendation, and retrieval.
Contribute to generative AI and agentic AI systems involving RAG pipelines, prompt engineering, and workflow orchestration.
Manage the full ML lifecycle from problem formulation, model training, deployment, monitoring to continuous improvement and collaborate with cross-functional teams to translate business needs into ML solutions.
4–6 years of experience building and deploying machine learning systems in production environments.
Strong foundation in machine learning, statistics, experimentation, and applied data science, especially NLP and transformer-based models.
Proficiency in Python and ML libraries such as Scikit-learn, PyTorch, TensorFlow, XGBoost, and experience with distributed data processing frameworks like Spark or Databricks.
Experience with model deployment, monitoring, lifecycle management; specific experience with LLMs, prompt engineering, RAG, and agentic AI workflows is preferred but not mandatory.
Experienced ML engineer skilled in full ML lifecycle and capable of independently delivering production-ready AI and ML solutions under moderate ambiguity.
Strong background in NLP and transformer architectures, with a focus on applied AI systems such as recommendation, ranking, and retrieval.
Comfortable working cross-functionally with product managers, engineers, and business stakeholders in dynamic, B2B SaaS or customer-facing AI environments.