Artificial Intelligence Engineer

- Proficient in Python (typing, pytest, logging, async) with hands-on experience in Mest, and skilled in SQL for data manipulation.

- Experienced with Git and CI/CD platforms (GitHub Actions, Jenkins, Azure DevOps, or GitLab CI), building automated pipelines for build, test, deployment, and rollback.

- Hands-on expertise with AWS, Azure, or GCP for compute, storage, IAM/RBAC, secrets management, and infrastructure automation using AWS CDK and Terraform.

- Utilized Databricks Asset Bundles to optimize data workflows and accelerate data processing.

- Knowledgeable in LLM frameworks such as LangGraph, CrewAI, or Strands, including building evaluation pipelines and applying guardrails for PII redaction, injection defense, and output validation.

- Implemented LLM observability with tracing, metrics (token, latency, cost), failure analysis, and managed cost/quota strategies including TPM/RPM limits and caching.

What You Must Have

- Bachelor's & Master Degree

- 4 years of experience

- Oral and written proficiency in English required

What Sets You Apart

- Designed and managed secure cloud networking architectures including VPCs with subnets, security groups, NAT gateways, and private connectivity (PrivateLink), ensuring robust traffic control and segmentation.

- Implemented scalable load balancing and DNS solutions using ALB/NLB and Route 53 to optimize application availability and performance.

- Ensured data security through encryption strategies (KMS/CMK), certificate management, and protecting data at-rest and in-transit.

- Administered cloud identity and access management across AWS IAM and Azure Entra ID, including roles, policies, cross-account access, app registrations, managed identities, and conditional access for secure authorization.

- Integrated SSO/SAML and automated secrets rotation to enforce strong authentication and streamline credential management, maintaining compliance with SOC 2, GDPR, and audit requirements.

- Developed and secured APIs with FastAPI using async endpoints, dependency injection, and middleware, incorporating OAuth2/OIDC, JWT validation, rate limiting, and CORS for controlled access and scalability.

- Leveraged messaging and event streaming platforms such as Kafka/MSK, Google Pub/Sub, SQS/SNS, EventBridge, and Kinesis to enable reliable, scalable event-driven architectures.

- Built robust data platforms using Databricks (Delta Lake, Unity Catalog), Snowflake, BigQuery, and orchestrated workflows via Airflow, Dagster, and Step Functions to streamline data processing and analytics pipelines.