Axim Digitech - Machine Learning Engineer

Job Description :

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.

Candidates 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.

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.