Lead the design and implementation of production AI/ML systems, including LLM applications, RAG pipelines, data and MLOps workflows, APIs, and AWS-based deployments. Responsibilities include model monitoring and retraining, multimodal data ingestion, enterprise integrations, CI/CD, governance, responsible AI guardrails, and secure handling of sensitive data.
This is a remote position.
Overview :
Build AI Systems (Core Responsibility)
- Design and implement end-to-end AI/ML solutions including LLM-based applications
- Build RAG pipelines using vector databases and enterprise data sources
- Build machine learning models that automate their training, validation, monitoring, and retraining
- Develop APIs and services to operationalize AI capabilities across the organization
Develop Data + AI Pipelines
- Build ingestion for multimodal content and transformation pipelines for structured and unstructured data
- Integrate AI workflows with enterprise systems (policy, claims, billing, etc.)
- Ensure data quality, traceability, reliability, and governance in all AI pipelines
Operationalize Models (MLOps)
- Implement CI/CD for AI/ML workflows
- Deploy, monitor, and maintain models in production
- Manage model versioning, performance monitoring, and retraining processes
Build on AWS
- Develop solutions using: Amazon SageMaker, AWS Lambda, S3, Glue, EKS, and related services
- Contribute to evolving use of AWS Bedrock
Apply Responsible AI Practices
- Implement guardrails for LLM-based systems (grounding, validation, safety)
- Ensure secure handling of sensitive data (PII, financial, etc.)
- Build systems aligned with enterprise governance and compliance standards
Qualifications:
Required
- 10+ years in software, data engineering, 5 years AI/ML engineering
- Hands-on experience building production AI/ML systems
- Experience with RAG pipelines, LLMs, or NLP-based systems
- Experience with AWS Bedrock or similar GenAI platforms
- Experience with data pipelines and distributed systems
- Experience deploying and operating systems in AWS
- Working knowledge of MLOps practices (CI/CD, monitoring, versioning)
Preferred
- Experience with vector databases (Pinecone, Weaviate, etc.)
- Experience in regulated industries (insurance, finance, healthcare)
- Exposure to microservices and containerized environments (Docker, Kubernetes)
Requirements
Qualifications:
Required
- 10+ years in software, data engineering, 5 years AI/ML engineering
- Hands-on experience building production AI/ML systems
- Experience with RAG pipelines, LLMs, or NLP-based systems
- Experience with AWS Bedrock or similar GenAI platforms
- Experience with data pipelines and distributed systems
- Experience deploying and operating systems in AWS
- Working knowledge of MLOps practices (CI/CD, monitoring, versioning)
Preferred
- Experience with vector databases (Pinecone, Weaviate, etc.)
- Experience in regulated industries (insurance, finance, healthcare)
- Exposure to microservices and containerized environments (Docker, Kubernetes)
Benefits
Diversity Inclusion:
At Exavalu, we are committed to building a diverse and inclusive workforce. We welcome applications for employment from all qualified candidates, regardless of race, color, gender, national or ethnic origin, age, disability, religion, sexual orientation, gender identity or any other status protected by applicable law. We nurture a culture that embraces all individuals and promotes diverse perspectives, where you can make an impact and grow your career.
Exavalu also promotes flexibility depending on the needs of employees, customers and the business. It might be part-time work, working outside normal 9-5 business hours or working remotely. We also have a welcome back program to help people get back to the mainstream after a long break due to health or family reasons.
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