Machine Learning Engineer
3 weeks ago
You will: Responsibilities Architect/Enable distributed compute aligning workloads to Small/Mid/High end GPUs. Leverage appropriate storage hardware and data formats to improve read/re-read efficiency. Identify and remediate latency contributors esp. IO bottlenecks, Inefficient Data shuffling, under/over utilized compute. Scale models by employing Distributed training using Data / Model Parallelism techniques. Parallelize inference processing to improve prediction latency. Provide Subject Matter Expertise in Graph and Vector databases for a variety of use cases that include Knowledge Graphs, RAG etc. Implement LLM observability and monitoring solutions.
Required Education and Experience Degree in Computer Science or Engineering Prior Experience with:- Docker, Kubernetes, and containerization. Distributed systems. Databricks ML Machine Learning Engineering Cloud (Azur Preferred) Expert level – Python, SQL
Preference will be given to candidates who in addition to required experience have:- Experience/Expertise with LLM Fine tuning, LLM Ops, Model Evaluation and Prompt Engineering Experience (or knowledge of) Mosaic ML, Ray Framework. Experience with Lang Chain or LlamaIndex Experience with any vector database.
Job Specifications:-
Authorities, Impact, Risk
Influence the data, AI and cloud journey for the bank. Influence the Sustainability roadmap for the bank.
Impact
Revenue generation thru New Business for Alternative Data Innovation 6 years of AI, Big Data and cloud expertise 3-4 years of Alternative data experience
Risk
Mitigate reputation risk thru AI driven Data Quality to ensure highest quality data and services are offered to clients
Mandatory Skillsets:-
2+ years of experience building machine learning training pipelines or inference services in a production setting. Experience with LLM deployment, fine tuning, training, prompt engineering, etc. Experience with LLM inference latency optimization techniques, e.g. kernel fusion, quantization, dynamic batching, etc. Experience with CUDA, model compilers, and other model-specific optimizations.
Preferred
Experience working with a cloud technology stack (eg. Azure or AWS). Experience building, deploying, and monitoring complex microservice architectures. Experience with Python, Docker, Kubernetes, and Infrastructure as code (e.g. terraform). Experience with LLMs, MLops Experience with distributed notebook environments like Databricks Experience building AI driven Data Quality frameworks and other data governance tools and capabilities Experience building meta data driven AI and statistical models for repeatable insight generation Experience building front to back data pipelines comprising of data ingestion , enrichment , data quality , Analytics and reporting Experience with Agile development methodology Experience with company KPIs and back testing of alternative data factors against company KPIs. Experience with NLP techniques and transfer learning frameworks like BERT Experience with using HuggingFace Model Artifacts
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