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Metro location, mid-level generalist title, and popular ML/LLM skillset increase applicant competition.
Core ML/NLP skills transfer across industries, though LLM/document-processing adds moderate specialization.
Explicit 1–3 years plus mandatory ML, LLM, and Python skills make filters stringent.
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Design, develop, and deploy AI/ML workflows and production-grade AI features leveraging LLMs, NLP, and agentic workflows with moderate guidance.
Build and maintain backend APIs and services, integrate AI models with external systems ensuring scalability, error handling, and observability.
Mentor junior engineers, participate in architectural decisions, code reviews, agile sprints, and troubleshoot issues across ML/AI stack.
Work experience: 1–3 years hands-on experience in AI/ML software engineering.
Strong proficiency in Python, with solid software engineering fundamentals including version control and testing.
Experience with ML frameworks (TensorFlow, PyTorch, scikit-learn) and NLP techniques (text classification, summarization, embeddings, information extraction).
Experience with LLM-based development, prompt engineering, RAG pipelines, APIs, and cloud platforms (AWS, Azure, or GCP).
Engineer comfortable owning AI/ML features end-to-end, balancing speed, quality, and scalability in production systems.
Experienced working with large language models and agentic AI workflows, capable of mentoring junior team members and collaborating on technical design.
Operating style includes pragmatic problem-solving, contribution to architectural discussions, and continuous learning in evolving AI/ML tools and techniques.