AI/ML Engineer (Product & Deployment)

We're Hiring AI/ML Engineer (Product & Deployment) Immediate Joiner

Role Summary: You are responsible for the end-to-end lifecycle of machine learning models. Unlike a Data Scientist who might stop at "insights," your goal is to build a production-ready system that stays "alive," accurate, and scalable.

Key Responsibilities

1. Model Development & Iteration: Build and fine-tune ML/DL models (Regression, Classification, CNNs, Transformers) using frameworks like PyTorch or TensorFlow.
2. Production Deployment (MLOps): Wrap models into scalable microservices using FastAPI or Flask and containerise them with Docker for cloud deployment.
3. Data Pipeline Engineering: Work with Data Engineers to build robust ETL pipelines (using Spark or SQL) to ensure high-quality training data.
4. GenAI & LLM Integration: Build RAG (Retrieval-Augmented Generation) pipelines and integrate LLMs (OpenAI, Llama) into core product features.
5. Monitoring & Maintenance: Implement "drift detection" and automated retraining to ensure models don't "decay" as real-world data changes.

Required Skills

• Tech Stack: Python (Expert), SQL, PyTorch/TensorFlow, Scikit-learn, Hugging Face.
• Infrastructure: AWS (SageMaker), GCP (Vertex AI), or Azure ML; Docker & Kubernetes.
• specialization: Hands-on experience with LangChain, Vector Databases (Pinecone/Milvus), and prompt engineering.
• Experience: Typically 13 years for 11LPA; 35 years for 15LPA+.

The "LPA" Breakdown in Bangalore

• 7L LPA: Common for strong freshers from premier institutes (IIT/NIT) or junior engineers (1-2 years) at mid-tier product companies.
• 10L LPA: The standard for mid-level engineers (3+ years) who have successfully moved a model from a "notebook" to a "live API".