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Strong brand, metro location, mid-level AI role with broad ML/MLOps skills increases candidate competition.
Specialized ML/LLM, MLOps, and production deployment requirements limit industry transferability.
Explicit 3–8 years, mandatory production deployments, and specific ML/MLOps tech stack make shortlisting stringent.
Build and deploy production-grade AI systems including LLMs, NLP, Computer Vision, and predictive analytics across business units.
Own end-to-end AI model lifecycle: design, training, fine-tuning, evaluation, A/B testing, and benchmarking.
Develop scalable data/model pipelines; deploy models on cloud platforms with CI/CD and observability; ensure service SLAs for latency, throughput, and cost.
3–8 years of experience in AI/ML/Applied ML Engineering with at least 2 production deployments.
Proficiency in Python, PyTorch or TensorFlow; cloud platforms (Azure/AWS/GCP); Docker and Kubernetes; CI/CD tools; MLflow or equivalent for MLOps.
Understanding of GenAI/LLMs including prompting, retrieval-augmented generation, vector databases (FAISS/PGVector/Weaviate/Milvus), and fine-tuning methods (LoRA/QLoRA).
Work mode is Work-From-Office with willingness to relocate to Mumbai preferred; Bachelor's or Master's degree in CS/EE/Math/AI or equivalent experience.
Experienced engineer with demonstrated ownership of end-to-end deployment of AI models in enterprise settings.
Strong system skills in cloud deployment, container orchestration (Docker/Kubernetes), and implementing CI/CD for ML pipelines.
Proficient in applied GenAI/LLM techniques and MLOps best practices, able to optimize inference performance under production SLAs.