ML Engineer Data Scientist

The Machine Learning Engineer will build and maintain production-grade AWS applications using Lambda, S3, Glue, ECS/EKS, and SageMaker, develop and automate end-to-end data and ML pipelines using Spark/PySpark and Airflow, implement LLM-based solutions including prompt engineering and RAG, apply GenAI techniques including context engineering and fine-tuning, support model training and inference, use engineering best practices including GitHub and CI/CD, and ensure compliance with security and data-governance standards. Strong emphasis is placed on model validation, thorough testing, and proactive issue resolution to ensure reliable, high-quality outputs across the organization. The role provides exposure to enterprise-scale data ecosystems, offering opportunities for professional growth while working alongside experienced data scientists and cross-functional technology teams.

7+ years in software engineering, data engineering, or ML engineering with strong AWS experience building production systems. Expertise in Python, PySpark, SQL, and LLM/GenAI. Proven experience with RAG, prompt engineering, and agentic frameworks. B.C.A., B.Tech/B.E., or B.Sc qualification. Prior experience mentoring junior team members and contributing to internal knowledge repositories will also be viewed favorably. Excellent verbal and written communication skills are essential, along with the ability to explain complex technical concepts clearly to both technical and non-technical stakeholders. Candidates should demonstrate strong analytical thinking, attention to detail, and the ability to manage multiple priorities in a fast-paced, deadline-driven environment.