MLOps Senior Engineer na None

Key callout from client on the position:
“This role is not for an AIML developer. We’re specifically looking for someone with the expertise outlined in the attachment—someone who can support the platforms our developers use, rather than build AI/GenAI solutions themselves. It’s essential that candidates are screened carefully against these requirements before resumes are submitted. While many engineers enjoy developing AI solutions, fewer are equipped or inclined to support the environments that enable them”

Job Title: MLOps Senior Engineer – Vector/LLDS Database & AI Platform

Focus

Core Technical Skills

- Vector Databases: Hands-on experience with Elasticsearch or similar; understanding of

similarity search, indexing strategies, and embedding management.

- Linux Systems: Strong command-line skills; shell scripting; system-level monitoring and

debugging.

- Python Programming: Proficient in automation scripting; experience in building AI

models, data pipelines, and OpenAI integrations.

- Big Data Technologies: Familiarity with Hadoop-based platforms like MapR and

Hortonworks.

Requirements

AI Platform & Production Support

- Experience supporting predictive AI workloads in production.

- Troubleshooting across data ingestion, model inference, and deployment layers.

- Familiarity with CI/CD pipelines and containerization (Docker, Kubernetes).

- On-call support for GenAI and predictive pipelines (1 week every 6–8 weeks).

- Understanding of enterprise disaster recovery (DR) solutions including backup and

restore.

Observability & Monitoring

- Ability to define and implement observability strategies for AI systems.

- Experience with tools such as Splunk, Grafana, ELK stack, OpenTelemetry.

- Proactive monitoring of model failures, latency, and system health.

Bonus Qualifications

- Multi-cloud Experience: Exposure to GCP and Azure environments.

- Data Science Lifecycle: Involvement in full-cycle projects including problem definition,

data exploration, modeling, evaluation, training, scoring, and operationalization.

- MLOps Principles: Understanding of model lifecycle management and collaboration with

data scientists to deploy solutions.

Benefits

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