Ai Ml Engineer

CirrusLabs

You have an entrepreneurial spirit. You enjoy working as a part of well-knit teams. You value the team over the individual. You welcome diversity at work and within the greater community. You aren't afraid to take risks. You appreciate a growth path with your leadership team that journeys how you can grow inside and outside of the organization. You thrive upon continuing education programs that your company sponsors to strengthen your skills and for you to become a thought leader ahead of the industry curve.

Senior / Lead Agentic AI & Data Science Engineer (Product Engineering)

Experience - 7-10 years

Experience

710 years total experience

Core Responsibilities

Agentic AI & LLM Systems

• Design, implement, and optimize

  Agentic AI architectures

  involving planning, reasoning, memory, tool-use, and orchestration.
• Build and manage

  multi-agent systems

  for complex workflows, automation, and decision intelligence.
• Implement

  Retrieval-Augmented Generation (RAG)

  pipelines with structured and unstructured data sources.
• Integrate AI agents with

  enterprise APIs, databases, SaaS platforms, and internal tools

  .
• Develop robust prompt strategies, agent workflows, fallback mechanisms, and evaluation pipelines.
• Deploy and operate

  LLM-based systems

  with cost, latency, reliability, and safety considerations.

Data Science & Machine Learning

• Build, train, evaluate, and deploy

  ML/DL models

  across NLP, structured data, time-series, recommendation, and predictive analytics.
• Perform

  data exploration, feature engineering, statistical analysis, and hypothesis testing

  .
• Design scalable

  training pipelines

  , experiment tracking, and model versioning.
• Monitor model performance, drift, bias, and data quality in production environments.
• Apply explainability and interpretability techniques where required.

Product Engineering & System Design

• Own the

  full AI product lifecycle

  : problem definition design development deployment monitoring iteration.
• Translate business and product requirements into

  scalable, modular, and maintainable AI solutions

  .
• Design

  distributed, fault-tolerant, and extensible architectures

  for AI platforms.
• Collaborate closely with

  product managers, UX, backend, frontend, and platform teams

  .
• Enforce engineering best practices including

  code quality, testing, documentation, and performance optimization

  .

Multi-Cloud & Infrastructure Engineering

• Design, deploy, and operate AI systems across

  AWS, Azure, and GCP

  (multi-cloud or hybrid).
• Use

  Docker, Kubernetes, Helm

  , and cloud-native services for scalable deployments.
• Implement

  Infrastructure as Code (IaC)

  using Terraform / CloudFormation.
• Leverage managed AI/ML services where appropriate (SageMaker, Vertex AI, Azure ML).
• Optimize cloud resource utilization and cost across environments.

Security, Governance & Reliability

• Ensure

  data security, privacy, and compliance

  across AI systems.
• Implement secure access control, secrets management, and encrypted data pipelines.
• Apply

  Responsible AI practices

  : bias detection, fairness, explainability, auditability.
• Design systems for

  high availability, disaster recovery, and fault tolerance

  .
• Establish governance standards for models, data, and AI agents.

Technical Leadership & Collaboration

• Provide technical guidance and mentorship to junior engineers and data scientists.
• Lead architecture discussions, technical reviews, and best-practice adoption.
• Drive innovation in AI/Agentic systems aligned with product and business goals.
• Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

Cloud, DevOps & MLOps

• Strong hands-on experience with

  AWS, Azure, and/or GCP

  (at least two preferred)

• Docker, Kubernetes, Helm

• CI/CD: GitHub Actions, GitLab CI, Jenkins
• MLOps tools:

  MLflow, Kubeflow

  , cloud-native ML platforms
• Monitoring and observability tools

Architecture & Distributed Systems

• Distributed systems and event-driven architectures
• Asynchronous processing and workflow orchestration
• Scalability, reliability, and performance engineering