AI/ML Ops Engineer

Job not on LinkedIn

🔥 3 minutes ago

🇮🇳 India – Remote

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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FyerX - Your Trusted Marketing Partner

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

📣 Marketing

🤝 B2B

Marketing • B2B

FyerX is a marketing agency based in Bangalore that specializes in helping businesses enhance their digital presence and improve online outcomes through tailored marketing strategies. With a passionate team of marketing experts, FyerX focuses on various aspects of digital marketing including social media marketing, branding, search engine optimization, and influencer marketing. They aim to fuel the growth of purpose-driven brands by developing integrated marketing plans that drive online growth and improve brand visibility.

📋 Description

• Design and automate end-to-end ML pipelines for continuous training and continuous deployment using Kubeflow, MLflow, or AWS SageMaker Pipelines • Orchestrate containerized model deployments on Kubernetes using KServe and Triton Inference Server • Configure low-latency inference endpoints and auto-scaling GPU/CPU clusters • Implement model tracking and data versioning foundations, including feature stores, model registries, and DVC • Build automated AI performance and data monitoring gates for model accuracy decay, data drift, concept drift, and processing latencies • Optimize inference environments using ONNX, TensorRT, and quantization strategies • Integrate generative AI and LLM operational frameworks with semantic caching, vector database scaling, and prompt validation pipelines • Govern machine learning access controls and security profiles, including data separation, model access tokens, and encryption protocols

🎯 Requirements

• 4 to 8 years of core software engineering, DevOps, or data engineering experience • 3+ dedicated years actively building and maintaining MLOps automation infrastructures • AWS Certified Machine Learning - Specialty, Google Cloud Certified Professional Machine Learning Engineer, or Databricks Certified Machine Learning Professional certification required • Strong technical mastery of Python programming • Experience with Docker and Kubernetes container orchestration • Experience with PyTorch, TensorFlow, and Hugging Face • Advanced SQL proficiency • Deep understanding of distributed system mechanics • Understanding of GPU resource management limits • Understanding of Shadow, Canary, and A/B model deployment patterns • Understanding of cloud provider API governance • Prior experience implementing RAG pipelines or fine-tuning open-source LLM layers in production preferred • Familiarity with Terraform preferred

🏖️ Benefits

• Remote work arrangement • Contract employment