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Metro location, popular ML title, and mid-level experience increase competition.
High domain specificity for NLP, RAG, and LLM engineering reduces cross-industry transferability.
Explicit 5+ years and mandatory NLP/LLM/toolset requirements create strict filters.
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Design, develop, and optimize NLP-driven AI solutions, including Retrieval-Augmented Generation (RAG) pipelines and agentic AI systems, for production deployment.
Fine-tune, prompt-engineer, and deploy large language models (LLMs) such as OpenAI, Anthropic, Falcon, and LLaMA tailored for domain-specific applications.
Collaborate with cross-functional teams to build scalable, reliable, and maintainable AI-first systems meeting enterprise standards, including implementing model observability and performance monitoring.
Master’s or Bachelor’s degree in Computer Science, Machine Learning, AI, or related field.
Minimum 5 years overall AI/ML experience, including at least 2 years in NLP and 1–2 years in Generative AI.
Proficiency in Python and NLP/ML frameworks such as LangChain, Transformers, LlamaIndex, SmolAgents.
Experience designing and deploying RAG pipelines, fine-tuning LLMs (LoRA, PEFT), and familiarity with ML observability tools and cloud AI services (AWS Sagemaker, Azure OpenAI).
Experienced in productionizing LLM-based agents and multi-step agent architectures with an emphasis on scalability and low latency.
Familiar with infrastructure-as-code (Terraform/CDK), DevOps practices for AI pipelines, and real-time NLP challenges like streaming and multi-turn dialogues.
Preference for candidates with domain knowledge in Electrification, Energy, or Industrial AI and experience integrating third-party models/APIs for generative AI use cases.