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Rapid7
Staff AI Engineer / Lead AI Engineer
Rapid7
Pune•Remote•8-11 Yrs•2 hours ago
Job description
Remote
Overview
The technologies we use include: • Python is used for analysis and modelling, with numpy, pandas, and scientific computing libraries.
Responsibilities
- Work with security teams to define, scope, and design research efforts for new threat detections, automations, and AI-driven workflows.
- Collaborate with data scientists to transform research into production-ready solutions, mentoring on both methods and execution.
- Research, build, and evaluate ML and generative/LLM models, including agentic and multi-step reasoning systems.
- Design and optimise agentic architectures, including tool-calling, decision-making, memory, orchestration, and evaluation frameworks.
- Partner with engineering teams to ship AI features, integrating models into high-scale systems.
- Deploy AI/ML workloads in AWS using SageMaker, Bedrock, Lambda, EKS, Step Functions, and related services.
- Contribute to our LLMOps and MLOps workflows, improving evaluation pipelines, observability, reproducibility, and governance.
- Support development of AI security features, improving reasoning, context understanding, automation, and analyst workflows.
- Embrace agile development, iterative experimentation, and collaborative problem solving.
- Mentor junior team members and uplift the technical bar for agentic AI and ML engineering.
- 8–12 years of experience as a Data Scientist, ML Engineer, or AI Engineer.
- Strong end-to-end practical expertise in ML/AI/DS.
- Able to explore, experiment, and deliver independently.
- scikit-learn is used for classical machine learning.
- PyTorch, TensorFlow, and Keras are used for deep learning.
- HuggingFace, LangChain, and Transformers are especially useful for LLMs.
- Pandas and NumPy are used for data preparation.
- A clear understanding of modelling approaches and their suitability for different problems is necessary.
- Strong communication skills and the ability to present technical concepts to diverse audiences are important.
- The ability to collaborate across engineering, data science, product, and security teams is necessary.
Qualifications
- Agentic AI and LLM expertise is highly preferred.
- Hands-on experience with agentic AI architectures, such as CrewAI, LangGraph, LangChain agents, tool-calling, and function-chaining, is preferred.
- Understanding of prompt engineering, evaluation frameworks, LLM observability, and guardrails is preferred.
- AWS and cloud experience is highly preferred.
- SageMaker training & inference
- Bedrock LLM integrations
- Lambda for serverless workflows
- EKS for containerized deployments
- S3, Glue, Athena, IAM best practices
- Strong experience in AWS ML ecosystem:
- Deployment of AI and machine learning models
- Implementing model risk management strategies, including model registries, concept/covariate drift monitoring, and hyperparameter tuning