Description :

- Design and implement CI/CD pipelines for AI and ML model training, evaluation, and RAG system deployment (including LLMs, vectorDB, embedding and reranking models, governance and observability systems, and guardrails).

- Provision and manage AI infrastructure across cloud hyperscalers (AWS/GCP), using infrastructure-as-code tools -strong preference for Terraform-.

- Maintain containerized environments (Docker, Kubernetes) optimized for GPU workloads and distributed compute.

- Support vector database, feature store, and embedding store deployments (e.g., pgVector, Pinecone, Redis, Featureform. MongoDB Atlas, etc).

- Monitor and optimize performance, availability, and cost of AI workloads, using observability tools (e.g., Prometheus, Grafana, Datadog, or managed cloud offerings).

- Collaborate with data scientists, AI/ML engineers, and other members of the platform team to ensure smooth transitions from experimentation to production.

- Implement security best practices including secrets management, model access control, data encryption, and audit logging for AI pipelines.

- Help support the deployment and orchestration of agentic AI systems (LangChain, LangGraph, CrewAI, Copilot Studio, AgentSpace, etc.

Must Haves :

- 4+ years of DevOps, MLOps, or infrastructure engineering experience.

- Preferably with 2+ years in AI/ML environments.

- Hands-on experience with cloud-native services (AWS Bedrock/SageMaker, GCP Vertex AI, or Azure ML) and GPU infrastructure management.

- Strong skills in CI/CD tools (GitHub Actions, ArgoCD, Jenkins) and configuration management (Ansible, Helm, etc.

- Proficient in scripting languages like Python, Bash, -Go or similar is a nice plus-.

- Experience with monitoring, logging, and alerting systems for AI/ML workloads.

- Deep understanding of Kubernetes and container lifecycle management.

Bonus Attributes :

- Exposure to MLOps tooling such as MLflow, Kubeflow, SageMaker Pipelines, or Vertex Pipelines.

- Familiarity with prompt engineering, model fine-tuning, and inference serving.

- Experience with secure AI deployment and compliance frameworks.

- Knowledge of model versioning, drift detection, and scalable rollback strategies.

Abilities :

- Ability to work with a high level of initiative, accuracy, and attention to detail.

- Ability to prioritize multiple assignments effectively.

- Ability to meet established deadlines.

- Ability to successfully, efficiently, and professionally interact with staff and customers.

- Excellent organization skills.

- Critical thinking ability ranging from moderately to highly complex.

- Flexibility in meeting the business needs of the customer and the company.

- Ability to work creatively and independently with latitude and minimal supervision.

- Ability to utilize experience and judgment in accomplishing assigned goals.

- Experience in navigating organizational structure.