Gen AI Engineer

Elevance Health•Mason, OH

20d•Hybrid

About The Position

Gen AI Engineer Location: This role requires associates to be in-office 1 - 2 days per week, fostering collaboration and connectivity, while providing flexibility to support productivity and work-life balance. This approach combines structured office engagement with the autonomy of virtual work, promoting a dynamic and adaptable workplace. Alternate locations may be considered if candidates reside within a commuting distance from an office. Please note that per our policy on hybrid/virtual work, candidates not within a reasonable commuting distance from the posting location(s) will not be considered for employment, unless an accommodation is granted as required by law. PLEASE NOTE: This position is not eligible for current or future visa sponsorship. The Gen AI Engineer is responsible for analyzing and modeling organizational data for the Artificial Intelligence (AI) function to draw business insights, which can be used to make business decisions. How You Will Make an Impact: Applies data extraction, transformation and loading techniques in order to connect large data sets from a variety of sources. LLM development and fine-tuning strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency. Develop roadmap and strategy for NLP, LLM, Gen AI model development and lifecycle implementation. Responsible for the design and development of custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM model development and ensure the end-to-end solution meets all technical and business requirements, and SLA specifications. Work closely with the MLOps team to create and maintain robust evaluation solutions and tools to evaluate model performance, accuracy, consistency, reliability, during development, and UAT. Identify and implement model optimizations to improve system efficiency. Collaborate closely with the MLOps, product teams, business stakeholders, machine learning engineers, and software engineers for the deployment of machine learning models into production environments, ensuring smooth integration, reliability and scalability. Ensure the use of standards, governance and best practices in ML model development, and adherence to model and data governance standards.

Requirements

Requires a Bachelor’s degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent degree and 4 or more years of experience; or any combination of education and experience in configuration management, which would provide an equivalent background.

Nice To Haves

Advanced Python proficiency.
4+ years of professional hands-on experience leveraging large sets of structured and unstructured data to develop data-driven tactical and strategic analytics and insights using ML, NLP, and computer vision solutions.
Demonstrated 4+ years hands-on experience with Python, SQL, Hugging Face, TensorFlow, Keras, PyTorch, and Spark.
Experience with GCP/AWS cloud platforms.
Strong knowledge of and measurable hands-on experience with developing or tuning Large Language Models (LLM) and Generative AI (GAI)
Experience with NLP, LLMs (extractive and generative), fine-tuning and LLM model development.
Experience developing and optimizing high-quality prompts for NLP applications.
Excellent written & verbal communication and stakeholder management skills.
4+ years project leadership experience including Agile project management, Scaled Agile Frameworks (SAFE).
LLM Infrastructure & Deployment: LLM serving platforms (vLLM, Text Generation Inference, FastAPI); Model quantization for LLMs (GPTQ, AWQ, bitsandbytes); GPU memory optimization techniques (tensor parallelism, pipeline parallelism); LLM caching strategies for inference optimization; RAG architecture design and implementation.
Advanced cloud infrastructure (AWS EKS/ECS, GCP GKE, Azure AKS) knowledge.
Containerization strategies for ML workloads; Canary deployments for ML models.

Responsibilities

Applies data extraction, transformation and loading techniques in order to connect large data sets from a variety of sources.
LLM development and fine-tuning strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency.
Develop roadmap and strategy for NLP, LLM, Gen AI model development and lifecycle implementation.
Responsible for the design and development of custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM model development and ensure the end-to-end solution meets all technical and business requirements, and SLA specifications.
Work closely with the MLOps team to create and maintain robust evaluation solutions and tools to evaluate model performance, accuracy, consistency, reliability, during development, and UAT.
Identify and implement model optimizations to improve system efficiency.
Collaborate closely with the MLOps, product teams, business stakeholders, machine learning engineers, and software engineers for the deployment of machine learning models into production environments, ensuring smooth integration, reliability and scalability.
Ensure the use of standards, governance and best practices in ML model development, and adherence to model and data governance standards.

Benefits

We offer a range of market-competitive total rewards that include merit increases, paid holidays, Paid Time Off, and incentive bonus programs (unless covered by a collective bargaining agreement), medical, dental, vision, short and long term disability benefits, 401(k) +match, stock purchase plan, life insurance, wellness programs and financial education resources, to name a few.

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