Senior Machine Learning Engineer

ML & AI System Design and Development

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