- 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 :

- Bachelors or Masters 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.