North East, PA · On-site +1
$96K - $132K/yr
Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector ... remote #LI-TS1 Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace. If you're ...
North East, PA · On-site +1
$96K - $132K/yr
Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector ... remote #LI-TS1 Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace. If you're ...
North East, PA · On-site +1
$96K - $132K/yr
Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector ... remote #LI-TS1 Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace. If you're ...
North East, PA · On-site +1
$96K - $132K/yr
Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector ... remote #LI-TS1 Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace. If you're ...
| Aspect | Remote Retrieval Augmented Generation | Remote Data Scientist |
|---|---|---|
| Credentials | AI/ML knowledge, programming skills | Statistics, programming, domain expertise |
| Work Environment | AI development, NLP projects | Data analysis, model building |
| Industry Usage | AI, NLP, machine learning | Tech, finance, healthcare |
| Search & Comparison | Often compared for AI roles involving language models | Compared for data analysis roles |
Remote Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with language generation, requiring expertise in AI, NLP, and programming. Remote Data Scientists analyze data, build models, and interpret results, often with statistical and domain knowledge. While both roles may work remotely and involve data handling, Retrieval Augmented Generation emphasizes AI model development, whereas Data Scientists focus on data analysis and insights.
For Remote Retrieval Augmented Generation jobs in Erie, PA, the most frequently searched job titles are:
Cities near Erie, PA with the most Remote Retrieval Augmented Generation job openings:
$96K - $132K/yr
Full-time
Re-posted 6 days ago
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Senior Engineer - LLMOps & MLOpsRole Overview
This is a high-stakes, execution-focused role within the Transformation Office. We are looking for a "day-one" engineer to own the production lifecycle of our AI initiatives. Your mission is to build the automated infrastructure that bridges our legacy data systems with modern AWS and Azure AI services. You will be responsible for the "Ops" of AI: ensuring that LLM applications, RAG pipelines, and traditional ML models are deployable, observable, and scalable in a multi-cloud environment.
Key Responsibilities
Multi-Cloud Pipeline Execution: Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio).
LLMOps Framework Implementation: Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization.
Legacy Data Connectivity: Build the engineering "pipes" to securely ingest and move data from legacy systems (Mainframes, SQL Server, on-prem DBs) into cloud-native MLOps workflows.
Automated Model Evaluation: Implement systemized frameworks for LLM evaluation (LLM-as-a-judge, ROUGE, METEOR) and traditional ML validation to ensure performance before deployment.
Observability & Monitoring: Deploy real-time monitoring for model drift, hallucination detection, latency, and token consumption to manage both quality and cost.
Infrastructure as Code (IaC): Manage all AI resources using Terraform or CloudFormation, ensuring the cloud posture is reproducible, secure, and follows a "Privacy by Design" mandate.
Advanced Analytics Integration: Partner with teams using platforms like Palantir, Databricks, or Snowflake to ensure a high-fidelity data flow between analytical ontologies and production models.
IT & Security Diplomacy: Work directly with central IT and Security to navigate IAM roles, VPC peering, and firewall configurations, clearing the path for rapid transformation.
Scalable Inference Engineering: Optimize model serving endpoints for high-throughput and low-latency, utilizing containerization (Docker/Kubernetes) and serverless architectures where appropriate.
Prompt & Model Versioning: Establish rigorous version control for prompts (PromptOps), model weights, and data snapshots to ensure 100% auditability and rollback capability.
Data Science Engineering: Support the data science lifecycle by automating feature stores, feature engineering pipelines, and the transition of experimental notebooks into hardened production microservices.
Security & Compliance Hardening: Implement automated scanning and guardrails (e.g., Bedrock Guardrails or Azure Content Safety) to prevent prompt injection and data leakage.
Qualifications
Education: Bachelor's degree in Computer Science or a related field required; Master's degree in a quantitative discipline highly desirable.
Proven Execution: 6+ years of engineering experience, with a minimum of 3 years strictly focused on MLOps or LLMOps in a production environment.
AWS & Azure Mastery: Deep, hands-on proficiency in both ecosystems. You must be able to configure Bedrock and Azure OpenAI services, including private networking and endpoint security, on day one.
Technical Stack: Expert Python, SQL, and PySpark. Extensive experience with containerization (Docker, Kubernetes) and orchestration tools (Airflow, Kubeflow, or Step Functions).
LLM Tooling: Professional experience with evaluation and observability frameworks like LangSmith, Arize Phoenix, or WhyLabs.
Data Science Flavor: A strong understanding of statistical validation, model evaluation metrics, and the ability to partner with Data Scientists to optimize model performance.
Transformation Mindset: The ability to move at the speed of a startup while maintaining the collaborative relationships required to function within a large-scale enterprise IT landscape.
#remote #LI-TS1
Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace.
If you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. You may be just the right candidate for this or other roles.