MLOps Engineer
Location: Bolingbrook, Illinois (Onsite)
No H1B
Job Description:
The MLOps Engineer is responsible for operationalizing, scaling, and maintaining enterprise AI/ML systems across cloud, hybrid, and on premise environments. The role focuses on enabling reliable delivery of LLM workloads, retrieval augmented generation (RAG), document intelligence, multimodal processing, and predictive/ML pipelines-supported by strong governance, observability, security, and automation.
Key Responsibilities:
- Build and automate end to end ML pipelines (data ingestion feature engineering training evaluation packaging deployment).
- Establish model CI/CD workflows including versioning, automated testing, canary/blue green deployments, and rollback strategies.
- Operationalize LLM based and RAG systems (embedding workflows, vector indexing, latency optimization, grounding quality checks).
- Productionize document processing and multimodal workflows (OCR parsing, enrichment flows, batch/stream scaling).
- Implement observability (data quality, drift, safety indicators, inference latency, error conditions).
- Enforce Responsible AI controls (auditability, reproducibility, governance metadata, lineage, approval workflows).
- Maintain secure serving environments (container hardening, IAM, secrets, network isolation).
- Optimize GPU/CPU utilization, autoscaling, throughput, and cost efficiency.
Create reusable templates, reference architectures, starter repos, and documentation.
Required Skills & Qualifications:
- Strong Python, CI/CD, Docker, Kubernetes.
- Experience operationalizing LLM, RAG, and predictive ML systems.
- Strong foundations in data engineering, schema governance, batch/stream pipelines.
- Security mindset (PII controls, secrets, network boundaries, auditability).
- Vertex AI (ML orchestration & CI/CD, training, tuning, deployment, model registry & monitoring).
- BigQuery / BigQuery ML (analytics & in warehouse ML).
- Cloud Composer + Dataflow (batch/stream ETL orchestration).
- GKE or Cloud Run (secure, scalable model serving).
- Artifact Registry + Cloud Build/Cloud Deploy (container & CI/CD).
Preferred Qualifications:
- Familiarity with agentic reasoning patterns and workflow chaining.
- Experience with LLM evaluation, grounding, bias/safety checks.
- Contributions to open-source ML/MLOps tooling.
| Skills Matrix |
| Skill | Last Used | Experience in Years/month | Rating (10 points) 1 = newbie 10 = expert | Hands on Exp. Yes/No |
| LLM | | | | |
| GKE or Cloud Run | | | | |
| BigQuery / BigQuery ML | | | | |
Navnish kumar
Sr. IT Technical Recruiter
Stellent IT Phone:
Email: navnish
Gtalk: navnishom
