MLOps Engineer

🕒 July 29

🇮🇳 India – Remote

⏰ Full Time

🟢 Junior

🟡 Mid-level

🤖 Machine Learning Engineer

👻 Ghost score 17%

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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EXL

WebsiteLinkedInAll Job Openings

10,000+ employees

🏥 Healthcare

🛡️ Insurance

📦 Logistics

💰 $2M Venture Round on 2015-01

Healthcare • Insurance • Logistics

EXL is a business consulting and services firm that focuses on leveraging data to enhance business operations and decision-making. With a strong emphasis on collaboration and adaptability, EXL partners with organizations to address their unique needs and culture while integrating data science and technology solutions. The company's areas of expertise include operations management, decision analytics, digital transformation, and various industries such as healthcare, finance, and insurance. EXL's mission is to help clients drive business evolution and maintain competitive advantage through tailored solutions and effective use of data.

📋 Description

• 2–4 years of hands-on experience in software/data/ML engineering in production environments • Very strong Python - clean, production-quality code (not just notebooks); sharp problem-solving and the aptitude to pick up MLOps practices quickly • Experience building and deploying APIs/services (FastAPI, Flask, or similar) and working knowledge of AWS (EC2, S3, Lambda) for hosting and serving • Understanding of the end-to-end ML lifecycle - training vs inference pipelines, deployment, and monitoring • Working knowledge of SQL and familiarity with PySpark; exposure to Databricks or comparable platforms, with the ability to read, refactor, and convert code for other environments • CI/CD and version-control fundamentals (Git, testing, rollback); familiarity with Docker; strong ownership and comfort with a broad, evolving scope and global stakeholders • Prior hands-on MLOps tooling experience (MLflow, model registries, drift detection, ML observability) • Experience supporting GenAI or LLM workloads operationally (model serving, inference pipelines, cost/performance tuning)

🎯 Requirements

• Master's or Bachelor's degree in Computer Science, Engineering, Math, Statistics, or a related field • 2–4 years of relevant hands-on experience; candidates who can join immediately will be prioritized