Senior Machine Learning Engineer
RemoteHyderabad, Telangana, India or Singapore
Job Summary
Develop and deploy machine learning models from initial research to production, ensuring scalability and performance in live environments for visibility, prediction, and demand forecasting. Own the end-to-end ML pipeline, including data processing, model development, testing, deployment, and continuous optimization using MLOps best practices. Build real-time prediction models with version control and performance tracking, performing feature engineering and tuning to maintain production readiness. Work directly with product teams to transform loosely defined problems into shipped features, pushing back on weak briefs and making independent calls when specs shift.
Required Qualifications
- Bachelor's, Master's, or PhD in Computer Science, Engineering, or a related field
- At least 5+ years of end-to-end and consistent building, deploying, and scaling machine learning models in production environments
- Hands-on experience productionising LLM-based systems
- Proven experience across the full product lifecycle, taking models from R&D to deployment in fast-paced environments
- Experience in a product-based company, preferably a startup with early-stage technical product development
- Strong expertise in Python and SQL
- Experience in cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
- Familiarity with real-time data processing, anomaly detection, and time-series forecasting in production
- Experience with large datasets and big data technologies like Spark and Kafka to build scalable solutions
- First-principles thinking and strong problem-solving, with a proactive approach to challenges
- A self-starter who takes ownership end to end and works autonomously to drive results
- Excellent communication, with the ability to convey complex technical concepts clearly and a strong customer-obsessed mindset
Desired Qualifications
- Bonus points for designing AI agents and multi-step workflows, tool/function calling, and grounding models on proprietary data through retrieval and context design - and treating prompts and model behaviour as engineering artifacts: versioning and prompt management, evaluation harnesses, guardrails, and monitoring output quality, latency and cost in live systems. We care about how you reason about system behaviour, reliability, and cost
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