AI ML Engineer Equipoptimizacions Software

3.0 - 5.0 years

15 - 25 Lacs

pune

Hybrid

Role & responsibilities Define and execute the AI/ML roadmap for personalized recommendation features. Develop and deploy ML models into production using tools like FastAPI, Docker, MLFlow , and Airflow . Build and optimize recommendation systems using collaborative filtering , content-based filtering , and knowledge-based techniques . Integrate agentic AI workflows using LLMs (OpenAI APIs, Vertex AI) and Retrieval-Augmented Generation (RAG). Work with structured and unstructured data including user behavior, travel logs, and merchant/product metadata. Use Perplexity-style LLM architectures to enhance explainability and product/merchant discovery. Collaborate across backend, frontend, and data engineering teams for end-to-end system integration . Ensure scalable and secure deployment on cloud platforms (AWS, GCP, or Azure). Preferred candidate profile 2 to 5 years of IT industry experience & min 2 years ML/AI model development, roadmap ownership, and production deployment. Proven experience deploying ML models with FastAPI, Docker, MLFlow , and Airflow . Strong hands-on expertise in Python , Pandas , scikit-learn , and recommendation libraries like LightFM or XGBoost . Solid understanding of recommender systems , including hybrid approaches combining collaborative and content-based filtering. Familiarity with agentic AI systems and LLM workflows (RAG, prompt engineering, conversational AI). Working knowledge of Vertex AI , OpenAI APIs , or Perplexity-style architectures for LLM-enhanced recommendations. Exposure to cloud environments (AWS/GCP/Azure) for training and serving ML models. Immediate availability is a must. Strong problem-solving skills, adaptability, and the ability to work independently in a fast-paced environment.

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