Back
Tier-1 brand, metro location, and broad generative-AI skill requirements increase applicant competition.
Requires specialized generative-AI, LLM, and ML engineering experience, limiting cross-industry transferability.
Multiple explicit mandates (10+ years, 5+ ML years, LLMs, cloud, vector DBs) make screening strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and deliver scalable generative AI services integrated across multiple applications and tenants, operating at production scale.
Drive system efficiencies via automation, capacity planning, performance tuning, monitoring, and root cause analysis.
Collaborate with Product Managers, Architects, Data Scientists, and Researchers to translate customer needs into innovative AI products, especially in Retrieval-Augmented Generation (RAG) and Enterprise Knowledge Graph domains.
10+ years of software engineering experience including 5+ years in machine learning engineering building AI systems or services.
Proficiency in Java and Python programming; experience with ML frameworks such as TensorFlow or PyTorch.
Experience with distributed scalable systems and modern data stack technologies like Spark, Flink, Hadoop, Kafka, Docker.
Familiarity with LLMs, prompt engineering, vector databases (Milvus/Pinecone), applied generative AI frameworks (LangChain, LlamaIndex), and public cloud AI/data services (AWS/GCP).
Experienced ML engineer with strong background in large-scale generative AI systems focused on NLP and complex unstructured data use cases.
Demonstrated ability to innovate and solve novel problems in AI system design and deployment at scale.
Collaborates across technical and business teams to align AI technology development with customer and product requirements.