MLOps Engineer

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: ₹271,719.81 - ₹1,166,266.45 per year

Work Location: In person