MLOps Engineer

7 days ago

West Mambalam, Tamil Nadu, India Infinite Technology Solutions Full time

Key Responsibilities1. ML Pipeline Development

  • Build and automate end-to-end ML pipelines (data ingestion → preprocessing → training → deployment).
  • Develop reusable workflows using tools like Kubeflow, Airflow, MLflow, DVC, Metaflow.
  • Implement scalable model training on cloud GPUs/CPUs.

2. Model Deployment & Serving

  • Deploy ML & LLM models to production using:
  • Docker, Kubernetes
  • AWS Sagemaker, GCP Vertex AI, Azure ML
  • ONNX, Triton Inference Server, Ray Serve
  • Implement high-availability model endpoints with autoscaling.

3. CI/CD for Machine Learning

  • Build automated CI/CD pipelines for ML workflows using GitHub Actions, GitLab CI, or Jenkins.
  • Integrate continuous training (CT) and continuous deployment (CD) for models.
  • Version management for code, data, and models.

4. Monitoring & Observability

  • Monitor model performance (accuracy drift, data drift, concept drift).
  • Set up logs, metrics, alerts using Prometheus, Grafana, ELK, or similar tools.
  • Maintain dashboards for model health and prediction quality.

5. Data Engineering & Governance

  • Collaborate with data engineers to maintain robust data pipelines.
  • Ensure data quality, lineage, versioning, governance, and compliance.
  • Manage feature stores (Feast, Tecton, Vertex Feature Store).

6. Infrastructure & Cloud Management

  • Build and maintain cloud infrastructure for ML workloads.
  • Optimize model serving costs and performance.
  • Manage containerized environments using Docker & Kubernetes.

7. Collaboration & Best Practices

  • Work closely with data scientists, engineers, and product teams.
  • Translate ML requirements into scalable infrastructure solutions.
  • Establish and enforce MLOps best practices across teams.

Required Skills & QualificationsTechnical Skills

  • Strong experience in Python, ML libraries, and API development.
  • Expertise with ML platforms: Airflow, MLflow, DVC, Kubeflow, Ray, Tecton.
  • Hands-on experience with Docker, Kubernetes, Terraform/Ansible.
  • Strong command of AWS/GCP/Azure cloud ML services.
  • Knowledge of CI/CD pipelines.
  • Experience with monitoring tools (Prometheus, Grafana, ELK).
  • Familiarity with data versioning, ETL pipelines, and feature stores.
  • Understanding of LLM Ops for deploying large language models.

Soft Skills

  • Strong communication and documentation abilities.
  • Analytical, problem-solving mindset.
  • Ability to collaborate with cross-functional teams.
  • Independent ownership of ML deployments and infra decisions.

Preferred Qualifications

  • Bachelor's or Master's in Computer Science, AI/ML, Data Science, or related fields.
  • Certifications: AWS ML Specialty, Google ML Engineer, Kubernetes CKA/CKAD.
  • Experience with LLM fine-tuning and vector databases.
  • Prior production experience with NLP/CV/LLM models.

Key KPIs

  • Uptime and availability of ML model APIs.
  • Deployment frequency and automation success rate.
  • Reduction in training/serving costs.
  • Monitoring and alert efficiency (drift detection, performance stability).
  • Faster lead time from model development to production.

Why Join Us

  • Work with cutting-edge AI/ML technologies.
  • Opportunity to build a world-class MLOps infrastructure.
  • Fast-paced environment with strong career growth.
  • Work on impactful AI projects across industries.

Job Types: Full-time, Part-time, Freelance

Pay: ₹271, ₹1,166,266.45 per year

Work Location: In person

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