- Experience: 3–5 years
- Location: Bangalore
- Notice Period: Immediate / October joiners
- CTC: 34.5 LPA
- Mandatory Skills: MLOps, Model Deployment, CI/CD, DevOps, QFlow

Role - MlOps + Dev

NP - Immediate or Oct Joiners.

Exp -3-5 Years - G8

Location - Bangalore (Hybrid)

Please share profiles start from Monday only, this is very urgent role.

Attached final selects profiles as sample.

MLOps Engineer

Role Overview

Ideal candidates will have 3-5 years of production experience with ML platforms (e.g., SageMaker, Azure ML, Databricks), and expertise in Kubernetesbased model serving and GitOps automation. You will champion reliability (SLOs/SLIs), compliance, and automationfirst practices across the ML lifecycle.

Key Responsibilities

- Design, develop, and document Infrastructure as Code (Terraform) for ML/LLM platform components on AWS/Databricks; implement secure, scalable foundations for data, compute, networking, and secrets.
- Build and maintain GitHub based pipelines (Actions/Workflows) for training, packaging, validation, and deployment of ML/LLM assets (models, evaluation suites, prompts, policies), using GitOps for environment promotion.
- Containerize models using Docker and deploy them primarily through managed endpoints (SageMaker/Azure ML); Kubernetes‑based serving (KServe/Triton/Seldon) is a plus.
- Operate model registries and feature stores; enforce versioning, lineage, and artifact governance via MLflow/Databricks and cloud native services.
- Implement logs/metrics/traces, performance profiling, and drift/quality monitors; define SLIs/SLOs and on call runbooks; drive incident response and post-mortems with accountability (business hours support rotation).
- Embed DevSecOps: secrets management, IAM/RBAC, vulnerability scanning, image signing, policy as code, least privilege access, backup/DR/resiliency patterns; align with enterprise security standards.
- Operationalize GenAI: prompt/content safety filters, evaluation harnesses (human in the loop), grounding/attribution logging,



token cost & latency tracking, and red teaming pipelines integrated into CI/CD.
- Monitor and optimize compute/storage/bandwidth and inference costs; implement right sizing, autoscaling, and caching strategies.
- Partner with Data Scientists to productize models; co design platform features with stakeholders; deliver documentation, templates, and knowledge transfers that accelerate safe reuse.
- Run operations (RUN): Troubleshoot escalations, improve monitoring, automate administration/IRP tasks, and continuously harden reliability, performance, and security across environments.

Required Skills & Qualifications
- Technical Experience:
- Understanding of DevOps concepts such as reference implementation enforcement, use of shared DevOps stacks, infrastructure optimization (performance, cost, HA, resiliency), release management (GitOps best practices), and QA automation frameworks.
- Strong knowledge of AWS ecosystems and Databricks integration.
- Proficiency in Terraform for developing, testing, and maintaining Infrastructure‑as‑Code to manage cloud services for ML engineering.
- Hands‑on experience with CI/CD using GitHub, GitHub Actions, and Workflow automation to support continuous integration, delivery, and deployment of ML assets.
- Strong experience with Docker; Kubernetes is a plus.
- MLflow (tracking/registry), model registries, feature stores, experiment tracking, and lineage management; Databricks and cloud native equivalents.
- Build pipelines for training, testing (unit/integration/e2e), evaluation, and deployment.
- Experience designing or contributing to infrastructure, application,



and performance monitoring (logs, metrics, dashboards) and supporting observability strategies.
- Ability to produce productive, maintainable code in Python; experience troubleshooting and extending Python‑based services.

- Consulting Experience:
- Proven track record in an IT consulting environment, engaging with large enterprises and MNCs in strategic data solutioning projects.
- Experience working with enterprise stakeholders in platform adoption, requirement clarification, effort sizing, and change management for ML platform rollouts.

- Leadership & Soft Skills:
- Strong collaboration and communication across Delivery and RUN.
- Excellent communication, documentation, and presentation skills.
- Strong problem-solving, analytical thinking, and strategic vision.

- Educational Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field.

- Preferred Certifications:
- AWS DevOps Engineer – Professional
- AWS Certified Machine Learning – Specialty (or Azure DevOps Engineer Expert)
- CKA (Certified Kubernetes Administrator), HashiCorp Terraform Associate

What We’re Looking For
- Self-starters who are highly motivated, ambitious, and eager to challenge the status quo.
- Builders who combine scientific rigor with pragmatic engineering and can balance accuracy, latency, and cost.
- Effective leaders who collaborate openly, freely share knowledge and elevate team performance.
- Straightforward, results-oriented individuals who value impact and accountability.
- Adaptable experts who stay on top of fast-evolving AI technologies and practices.

Why Join Us?
- Opportunity to shape and build an AI product portfolio that delivers meaningful business impact for SE Regions.
- Work alongside a motivated and innovative team that values learning, ownership, and excellence.
- Thrive in a culture that challenges the status quo and embraces diverse perspectives.

📌 MLOps+Dev (Bengaluru)
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