Job Description :Role & Responsibilities :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 :- 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.- Solid 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. (ref:hirist.tech)

📌 Senior Machine Learning Engineer (Noida)
🏢 Skyleaf
📍 Noida