AI/ML Engineering Lead
The Toro Company (company site, opens in a new tab)6+ yrsPuneOn-siteDirect career pageEst. 18–30 LPA
Posted 21h ago
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Job description
Structured overview of role & requirements
About This Role
- Design, develop, validate, deploy, and continuously improve AI and machine learning solutions including Generative AI, RAG, and agentic AI applications for business use cases such as predictive analytics, classification, recommendation, anomaly detection, and forecasting.
- Build and maintain scalable, secure AI services, APIs, data pipelines, and implement MLOps/GenAIOps practices including CI/CD, versioning, monitoring, and lifecycle management of models and prompts.
- Collaborate cross-functionally to translate business needs into AI problem statements and production-ready solutions while ensuring Responsible AI, security, governance, and operational reliability.
Minimum Requirements
- Bachelor’s degree in Data Science, Machine Learning, Statistics, Computer Science, Applied Mathematics, Engineering, or related quantitative/technical field.
- 6+ years of hands-on experience in Machine Learning, Data Science, AI Engineering, and/or advanced analytics.
- Experience developing, validating, deploying machine learning solutions including production monitoring and optimization; Hands-on expertise with Generative AI or agentic AI applications (RAG, embeddings, vector search, tool calling).
- Proficiency in programming languages and tools such as Python, R, Spark, Scala, SQL, and strong knowledge of supervised/unsupervised ML techniques and working with structured and unstructured data.
Ideal Candidate Profile
- Experienced in end-to-end AI/ML solution delivery including technical ownership from model development to production monitoring and troubleshooting in enterprise environments.
- Skilled in advanced AI/ML engineering practices such as MLOps, GenAIOps, and managing production Generative AI solutions with considerations for scalability, security, cost, and Responsible AI controls.
- Comfortable working cross-functionally with business, data science, security, legal, and governance teams to align AI implementations with enterprise standards and regulatory requirements.