We are seeking a highly experienced and technically proficient Machine Learning Engineer to take ownership of our production ML infrastructure.

This is a crucial MLOps-focused role responsible for designing, building, and maintaining robust, scalable production-grade ML pipelines.

The ideal candidate will leverage expertise in NLP, distributed systems, and cloud-native architectures to ensure our machine learning models deliver reliable, continuous value.

Key Responsibilities & Technical Deliverables :

- ML Pipeline Architecture: Build, architect, and maintain end-to-end ML workflows using modern frameworks and best practices.

- This encompasses data ingestion, feature engineering, training, validation, and serving.

- Deployment & Orchestration: Lead the deployment of models into production using containers (Docker and Kubernetes).

- Utilize advanced orchestrators like Airflow or Vertex AI Pipelines for scheduled and event-driven execution.

- Distributed Systems: Work effectively with distributed systems and big data technologies (Spark) to handle large-scale data processing and model training efficiently.

- NLP & Model Serving: Focus on building and deploying robust solutions in Natural Language Processing (NLP).

Implement low-latency model serving layers using modern frameworks like FastAPI.

- LLM & Vector Integration: Maintain and integrate nascent technologies, including exploring and deploying models based on LLM architectures and managing high-scale data retrieval using Vector Databases.

- MLOps & Automation: Ensure models are production-ready by integrating advanced MLOps principles, guaranteeing continuous delivery, monitoring, and robust system performance.

Required Skills & Technical Expertise :

- Programming Languages (Mandatory): High proficiency in Python (for ML development) and strong working knowledge of Java (for system integration/backend).

- Expertise in advanced SQL is required.

- ML Frameworks: Hands-on experience with major frameworks like TensorFlow and PyTorch.

- Backend & Serving: Experience with REST API design and implementation using frameworks like FastAPI.

Infrastructure & MLOps :

- Strong knowledge of CI/CD pipelines, Docker, and Kubernetes.

- Practical experience with Infrastructure as Code (IaC) tools, particularly Terraform.

- Expertise in working with Spark and orchestrators (Airflow/Vertex AI).

- Cutting-Edge Exposure: Exposure to Vector Databases and LLM-based architectures is highly valued