AI/ML Engineer - Python/NLP
Description
Role Overview :
We are looking for a strong AI/ML Engineer to build production-grade AI solutions.
Mandatory Experience :
- 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.
- Strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
- Experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
- Hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
- Experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
- Hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
- Experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
Preferred Experience :
- Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.
- Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems.
- Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.
- Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies.
Mandatory Pedigree :
- B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are considered.
Skills
- AI
- Airflow
- Analytics
- AWS
- Azure
- CI/CD
- Cloud
- Cloud Native
- Data Science
- Deep Learning
- Docker
- Embeddings
- Feature Engineering
- Fintech
- GCP
- Generative AI
- Git
- Keras
- Kubeflow
- Kubernetes
- LLM
- LLMOps
- Machine Learning
- Mistral
- MLflow
- MLOps
- NLP
- Prefect
- PySpark
- Python
- PyTorch
- RAG
- Recommendation Systems
- SaaS
- scikit-learn
- Semantic Search
- Spark
- SQL
- TensorFlow
- Vector Databases
- Vector Search