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Tier-1 brand and Bangalore metro increase density, but senior specialized ML skills narrow the candidate pool.
Specialized generative AI and vector DB expertise favors ML-focused backgrounds, moderately limiting cross-industry transferability.
Explicit 10+ years plus 5+ ML years and mandatory LLM, cloud, and pipeline skills enforce strict filters.
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Design and develop scalable generative AI systems and services including Retrieval-Augmented Generation (RAG), Enterprise Knowledge Graph, and fine-tuned LLM deployments for Salesforce applications.
Drive system efficiencies such as automation, capacity planning, performance tuning, monitoring and root cause analysis in production environments serving thousands of tenants.
Collaborate with product managers, architects, data scientists, and researchers to translate customer requirements into prototypes and production AI platforms used by millions.
10+ years software engineering experience with at least 5 years in machine learning engineering building AI systems or services.
Strong programming skills in Java and Python; experience with ML frameworks like TensorFlow or PyTorch.
Proven experience with distributed scalable systems and data processing frameworks such as Spark, Flink, Hadoop, Kafka, and Docker.
Familiarity with LLMs, prompt engineering, vector databases (Milvus, Pinecone), generative AI frameworks (LangChain, LlamaIndex, RAG pipelines) and cloud-native AI services (AWS/GCP).
Experienced senior ML engineer comfortable with end-to-end AI product lifecycle including design, implementation, and production at scale.
Strong background in generative AI, NLP, and advanced ML techniques including deploying enterprise-grade LLMs and fine-tuning models.
Able to work cross-functionally across product, research, and engineering teams to deliver innovative AI solutions impacting millions of users.