Role Overview

Seeking an experienced ML Engineer to design, build, fine-tune, and productionize classical ML and GenAI solutions. You will own end-to-end deliveryfrom problem framing and data preparation to deployment, monitoring, and iteration—primarily on Google Cloud (Vertex AI, BigQuery). The role requires hands-on experience with RAG architectures, LLM prompt engineering, and robust MLOps practices. You will collaborate directly with customers, translate requirements into secure, scalable architectures, and lead project execution. This is a night-shift position aligned to 8am–5pm EST.

Required Qualifications

- 5-7 years overall industry experience with 5+ years of core software engineering building secure, scalable, performant applications.
- 3+ years hands-on experience designing, building, and deploying ML applications in production.
- Proficiency in Python and standard data science libraries: NumPy, Pandas, Scikit-learn.
- Hands-on experience with ML frameworks: TensorFlow or PyTorch; plus XGBoost.
- Solid foundation in at least one deep learning area: NLP, Computer Vision, or related.
- Proven experience building production-grade RAG systems and engineering prompts for LLMs.
- Cloud experience on GCP, including Vertex AI and BigQuery; building ML pipelines and serving endpoints.
- MLOps expertise: CI/CD for ML, model registry/versioning, monitoring, logging, data/Concept drift detection, alerting, and rollback.
- Data and feature engineering: data cleaning, handling outliers and class imbalance,



understanding distributions, and creating high-quality features.
- Excellent communication and customer-facing collaboration skills; ability to take ownership and lead delivery.
- Willingness and availability to work night shift aligned to 8am–5pm EST.
- Preferred certifications: Google Cloud Professional Machine Learning Engineer, Google Cloud Generative AI Engineer, TensorFlow Developer.
- Preferred: Experience integrating ML pipelines with existing data processing pipelines.

Responsibilities

- Lead end-to-end ML development: problem framing, data preparation, model training/fine-tuning, evaluation, deployment, and post-production support.
- Design, implement, and optimize RAG architectures (chunking, embeddings, retrieval, ranking) and iterate on prompts to improve LLM response accuracy.
- Build, automate, and monitor scalable ML pipelines on GCP (Vertex AI Pipelines, BigQuery, Cloud Storage) and establish CI/CD and model registry/versioning.
- Implement observability for models: performance monitoring, data and concept drift detection, alerting, and remediation playbooks.
- Collaborate with customers and engineering teams to translate requirements into secure, performant, and cost-effective cloud architectures; document decisions and designs.
- Integrate ML workflows with data processing pipelines and platform tooling as needed.
- Provide night-shift support aligned to 8am–5pm EST for customer-facing engagements and production operations.

📌 Ml Engineer (Hyderabad)
🏢 Mobilution It Systems
📍 Hyderabad