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Mid-level, metro location, and broad skillset attract many applicants despite niche LLM/Voice focus.
ML/AI expertise is transferable, but LLM fine-tuning and Voice AI require domain-specific experience.
Multiple mandatory technical skills, explicit 3–4 year requirement, and LLM/Voice expertise increase filtering strictness.
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End-to-end ownership of designing, building, and productionizing scalable AI systems across GenAI, AI platforms, Voice AI, LLM fine-tuning, ML training, and ML pipelines.
Develop and deploy production-ready GenAI applications using advanced methods including LLMs, RAG, agents, prompt engineering, and workflow orchestration.
Design and implement AI platform capabilities and voice AI systems ensuring safe, governed, scalable AI model usage and real-time conversational AI integration.
3 to 4 years hands-on AI/ML experience specifically in GenAI or ML engineering.
Strong programming skills in C, C++, and Python.
Experience with ML/LLM frameworks and fine-tuning tools such as PEFT, LoRA, or QLoRA.
Familiarity with containerization (Docker), workflow orchestration tools (Airflow, Prefect, Kubeflow), and deploying production-ready AI systems.
Proven track record working across application engineering, ML engineering, and AI platform infrastructure for production-grade systems.
Experience specifically with large language models including fine-tuning and deployment at scale.
Comfortable managing complex AI pipelines spanning data prep, training, evaluation, deployment, and monitoring in an end-to-end manner.