Flexera saves customers billions of dollars in wasted technology spend. A pioneer in Hybrid ITAM and FinOps, Flexera provides award-winning, data-oriented SaaS solutions for technology value optimization (TVO), enabling IT, finance, procurement and cloud teams to gain deep insights into cost optimization, compliance and risks for each business service. Flexera One solutions are built on a set of definitive customer, supplier and industry data, powered by our Technology Intelligence Platform, that enables organizations to visualize their Enterprise Technology Blueprint™ in hybrid environments—from on-premises to SaaS to containers to cloud.

We’re transforming the software industry. We’re Flexera. With more than 50,000 customers across the world, we’re achieving that goal. But we know we can’t do any of that without our team. Ready to help us re-imagine the industry during a time of substantial growth and ambitious plans? Come and see why we’re consistently recognized by Gartner, Forrester and IDC as a category leader in the marketplace. Learn more at flexera.com

We are looking for a Senior AI/ML Engineer to design and deploy generative AI and machine learning solutions across AWS, Azure, and Databricks environments. The role combines software engineering, machine learning, and cloud architecture to build production AI systems. You will collaborate with data scientists, engineers, product managers, and business stakeholders to deliver impactful AI solutions.

Key Responsibilities

Generative AI and LLM Integration
Build production applications using Amazon Bedrock models such as Claude, Titan, Llama, and Mistral.
Develop AI agents using Bedrock Agents, action groups, guardrails, and Prompt Flow orchestration.
Design prompt engineering strategies, evaluation frameworks, and responsible AI controls including filtering and bias detection.

RAG and Vector Search
Design scalable retrieval augmented generation architectures.
Implement vector search using OpenSearch Serverless and pgvector with Aurora PostgreSQL or Amazon RDS.
Develop embedding pipelines, chunking strategies, hybrid search, re ranking, and metadata filtering.

Cloud Architecture
Build serverless AI systems using Lambda, API Gateway, Step Functions, and EventBridge.
Develop ML pipelines using SageMaker, Feature Store, and SageMaker Pipelines.
Design data pipelines using Glue, Athena, and Redshift.
Implement secure VPC architectures and infrastructure as code using AWS CDK or CloudFormation.
Set up observability using CloudWatch and X Ray.

Production Engineering
Create CI/CD pipelines using GitHub Actions.
Implement model testing, monitoring, versioning, and deployment strategies.
Build alerting and incident response for ML pipelines.

Required Qualifications

Education
Bachelor’s degree in Computer Science, Machine Learning, Data Science, Mathematics, or related field.

Experience
5+ years in machine learning, data engineering, or software engineering.
3+ years working with cloud platforms such as AWS, Azure, or Databricks.
2+ years working with generative AI and large language models.
Experience deploying ML systems in production and building RAG systems with vector databases.

Technical Skills
Strong Python and SQL skills.
Experience with PyTorch, TensorFlow, or Hugging Face.
Familiar with LangChain, LlamaIndex, or Semantic Kernel.
Experience building APIs using FastAPI, Flask, or Django.
Knowledge of Docker and CI/CD tools.

Preferred
AWS Solutions Architect certification.
Experience with conversational AI, document processing, recommendation systems, NLP, computer vision, or time series.