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.