Position Details: Senior MLOPS Engineer

Description:

Position name : Senior MLOPS Engineer

About This Role

Join our Engineering team as a Senior MLOps Engineer owning the platform that takes our AI

models from notebook to production.

You will design and operate the infrastructure, pipelines,

and tooling that support Large Language Models (LLMs), Retrieval-Augmented Generation

(RAG) systems, agentic workflows, and classical machine-learning models across multiple use

cases.

You will ensure every model is reproducible, monitored, cost-efficient, and compliant with

enterprise security and regulatory standards.

This is a hands-on senior role for someone who

thrives at the intersection of software engineering, DevOps, and machine learning in a regulated

fintech environment.

Key Responsibilities

• ML Platform Engineering: Design, build, and operate the MLOps platform supporting

model training, experimentation, deployment, and serving for LLMs and classical ML

models

• CI/CD for ML: Build automated pipelines for model training, evaluation, packaging, and

promotion across environments, with reproducible builds and environment parity

• Model Serving & Scaling: Operate high-throughput, low-latency inference infrastructure

(GPU and CPU) using tools such as Triton, TorchServe, vLLM, or Text Generation

Inference (TGI), with autoscaling and multi-tenant isolation

• Monitoring & Observability: Instrument models and services for performance, drift,

data quality, hallucination, bias, latency, and cost; build alerting and dashboards for AI

reliability

• Feature Stores & Data Pipelines: Stand up and maintain feature stores (Feast, Tecton,

or equivalent) and streaming/batch pipelines that serve real-time, low-latency inference

• Model Registry & Governance: Implement model registries, versioning, lineage

tracking, approvals, and audit trails to meet regulatory and internal governance

requirements

• Infrastructure & IaC: Manage cloud and on-premise infrastructure using Infrastructure

as-Code (Terraform, Helm), with hardened Kubernetes clusters, networking, and secrets

management

• Security & Compliance: Enforce secure model supply chains, Personally Identifiable

Information (PII) controls, access management, and compliance with applicable data

protection and financial-services regulations

• Cost & Performance Optimization: Optimize GPU/CPU utilization, inference cost per

request, caching and batching strategies, and capacity planning

• Collaboration & Enablement: Partner with AI engineers, data engineers, DevOps, and

security teams to provide self-service tooling, reusable templates, and sound operational

practices

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related

field

• Minimum of 5 years of professional experience in software or platform engineering, with

at least 2 years focused on MLOps or production machine-learning systems

• Strong Python and shell scripting; solid software engineering practices including testing,

code reviews, and version control

• Deep experience with Docker and Kubernetes in production, including GPU scheduling

and multi-tenant workloads

• Hands-on experience with MLOps tooling such as MLflow, Kubeflow, Airflow, Dagster,

Prefect, SageMaker, Vertex AI, or Azure ML

• Proven experience deploying and operating model-serving stacks (e.g., Triton,

TorchServe, vLLM, TGI, KServe, BentoML)

• Strong experience with CI/CD platforms (GitHub Actions, GitLab CI, Jenkins, or Argo

Workflows)

• Infrastructure-as-Code proficiency with Terraform and/or Helm
• Experience with monitoring/observability stacks (Prometheus, Grafana, OpenTelemetry,

Datadog) and ML-specific tools (Evidently AI, WhyLabs, Arize, or equivalent)

• Working knowledge of cloud platforms (AWS, Azure, or GCP) and at least one

production deployment on private or sovereign cloud

• Experience with vector databases (Pinecone, Weaviate, Milvus, or pgvector) and feature

stores (Feast, Tecton, or equivalent)

Preferred Qualifications

• Experience operating LLM and RAG systems in production, including prompt/response

logging, guardrails, and safety monitoring

• Experience with GPU optimization, quantization, distillation, or fine-tuning pipelines

(LoRA, QLoRA)

• Exposure to responsible-AI tooling for bias, fairness, and explainability monitoring
• Familiarity with fintech, banking, or other regulated-industry environments
• Knowledge of data protection regulations (e.g., PDPA, GDPR, or equivalent)
• Experience with service mesh, API gateways, and zero-trust networking in AI platforms

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