AI / Machine Learning Engineer

Responsibilities

• Design, train, evaluate, and deploy scalable machine learning models and predictive data pipelines.
• Implement automated data preprocessing, feature engineering, and hyperparameter tuning frameworks.
• Deploy low-latency, containerized model inference APIs and microservices using FastAPI, Docker, and Kubernetes.
• Integrate vector search indices and embedding models with enterprise backend systems and SaaS applications.
• Monitor production model performance, latency, data drift, and establish automated continuous training (CT) loops.
• Collaborate with cross-functional software teams to identify high-value AI opportunities across client products.

Skills

• 2 to 5 years of hands-on professional experience developing and deploying machine learning models in production.
• Strong proficiency in Python and modern ML libraries: NumPy, Pandas, Scikit-Learn, PyTorch, or TensorFlow.
• Working experience with vector databases (Pinecone, Milvus, Qdrant, Chroma) and semantic similarity search.
• Hands-on experience deploying models on cloud platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
• Familiarity with MLOps best practices, versioning (MLflow, DVC), and containerization (Docker).
• Strong foundational knowledge of linear algebra, probability, statistics, and neural network architectures.

Qualifications

• B.Tech / B.E. / M.Tech / MCA in Computer Science, Data Science, AI/ML, or related quantitative field.

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