Machine Learning Engineer

Role Overview: We are looking for a Senior Machine Learning Engineer with 5+ years of experience to design, build, and deploy production-grade ML systems. You will bridge the gap between experimental data science and scalable software engineering, ensuring our models don't just work in a notebook, but thrive in a high-traffic production environment.

Key Responsibilities

• End-to-End Model Development: Design and implement machine learning models (Supervised, Unsupervised, and Deep Learning) to solve complex business problems.
• GenAI & LLM Integration: Fine-tune Large Language Models (LLMs) and implement Retrieval-Augmented Generation (RAG) architectures for enterprise applications.
• MLOps & Deployment: Build and maintain automated CI/CD pipelines for ML (using tools like Kubeflow, MLflow, or SageMaker) to manage model versioning, testing, and deployment.
• Data Engineering: Architect scalable data pipelines to ingest, clean, and preprocess massive datasets using Spark, Flink, or SQL.
• Performance Optimization: Monitor models in production to detect data drift and performance degradation; optimize inference latency for real-time applications.
• Mentorship: Lead technical design reviews and mentor junior engineers on best practices in coding and algorithmic selection.

Candidate’s Profile:

• BE/B Tech, BCA/MCA with 5+ Years’ should demonstrate a transition from Model Centric (focusing on accuracy) to Data & System Centric (focusing on reliability and scalability).
• Ready to work in Hyderabad, Bangalore
• Ready to join within 15 days
• Programming: Mastery of Python (clean, modular, and PEP8 compliant) and familiarity with compiled languages like Go or C++ for performance-critical components.
• Frameworks: Deep expertise in PyTorch or TensorFlow, and Scikit-learn for traditional ML.
• Cloud Architecture: 3+ years of experience with AWS, GCP, or Azure AI services (e.g., Vertex AI, Bedrock, or Azure ML).
• Infrastructure: Proficiency with Docker and Kubernetes for containerizing and scaling ML workloads.
• Vector Databases: Experience with Pinecone, Weaviate, or Milvus for managing embeddings in LLM workflows.

2. Soft Skills & Leadership

• Pragmatism: The ability to decide when a simple Linear Regression is better than a complex Transformer.
• Stakeholder Communication: Can explain "Precision vs. Recall" to a Product Manager without using a single equation.
• Product Mindset: Understanding that a model is only as good as the business value it generates.

3. Education & Certifications

• Education: Master’s or PhD in Computer Science, Statistics, or Math (or a Bachelor’s with a significant portfolio of shipped products).
• Certifications (Bonus): Google Professional ML Engineer, AWS Certified Machine Learning – Specialty, or specialized NLP/Deep Learning certifications.