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using a mix of classical ML models and LLM-based approaches.\n\n- Select appropriate techniques for each problem, balancing accuracy, performance, cost, and operational complexity.\n\n- Build and train models for tasks such as classification, similarity matching, extraction, normalization, and ranking.\n\n- Develop LLM workflows including embeddings, prompt design, and retrieval-augmented generation where applicable.\n\nData Engineering and Model Readiness : \n\n- Own data preparation for ML workloads, including data profiling, cleansing, deduplication, labeling, and validation.\n\n- Work with structured and unstructured datasets across relational databases, data lakes, and document sources.\n\n- Define and maintain training, evaluation, and inference datasets to support reliable model performance.\n\nProduction Integration and MLOps : \n\n- Integrate ML and LLM inference into Java/Spring backend services via APIs, async workflows, or batch processes.\n\n- Deploy and operate ML services on AWS using containers and managed services.\n\n- Implement model versioning, experiment tracking, monitoring, and retraining processes.\n\n- Ensure reliability, scalability, and observability of ML systems in production.\n\nTechnical Ownership and Collaboration : \n\n- Act as a senior technical contributor for ML and AI-related design and implementation decisions.\n\n- Collaborate closely with backend, data, and platform engineers to deliver production-ready systems.\n\n- Define and standardize engineering practices for building, deploying, and operating ML and LLM systems in production.\n\n- Provide guidance on when ML or LLM approaches are appropriate versus simpler alternatives.\n\nRequired Qualifications : \n\n- Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field.\n\n- 5+ years of experience building and deploying ML systems in production.\n\n- Strong Python skills with hands-on experience in classical ML frameworks and modern ML tooling.\n\n- Solid understanding of statistics, ML algorithms, and model evaluation techniques.\n\n- Experience working with data pipelines, data quality issues, and large datasets.\n\n- Familiarity with LLM concepts such as embeddings, prompt design, and RAG-style architectures.\n\n- Experience integrating ML systems into backend services and cloud environments (AWS preferred).\n\n- Ability to collaborate effectively with Java/Spring backend and platform teams.\n\nPreferred / Nice-to-Have : \n\n- Experience with NLP or text-heavy ML problems.\n\n- Hands-on exposure to open-source or hosted LLMs and vector search systems.\n\n- Experience with hybrid ML systems combining rules, models, and LLMs.\n\n- Prior experience in enterprise or B2B SaaS platforms.\n\n- Familiarity with data governance, security, and PII handling.\n\nSkills\nArtificial Intelligence, Machine Learning, System Design, LLM, MLOps, 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