Must have

• AWS

• Azure

• Cloud

• Spark

• TensorFlow

• Scala

• Python

• AI

• Machine Learning

• NumPy

• PyTorch

• SQL

• PostgreSQL

• MySQL

• English (C1)

Nice to have

• Kafka

• RabbitMQ

Requirements description

• Strong proficiency in Python and machine learning/NLP frameworks (knowledge of scikit-learn, spaCy, NLTK, or Gensim is a plus) .

• Proficiency in processing and analyzing large-scale datasets with Pandas, NumPy, and Dask, and strong SQL skills with databases such as PostgreSQL or MySQL (NoSQL experience with MongoDB or Cassandra is an advantage).

• Hands-on experience with vector search and semantic retrieval systems, preferably FAISS (or Pinecone, Weaviate, or Milvus).

• Practical experience deploying ML models and pipelines on a major cloud platform, such as AWS, Azure, or GCP.

• Experience with vector databases and semantic search.

• Hands-on experience with MLOps, CI/CD, containerization, and orchestration (Docker, Kubernetes).

• Knowledge of web frameworks (FastAPI, Flask) and API development (REST, GraphQL).

• Strong understanding of cloud security, authentication, and access management.

• Experience with monitoring and observability tools, such as Prometheus, Grafana, or the ELK Stack.

• Experience with modern AI/LLM frameworks, such as LangChain, LangGraph, Semantic Kernel, or Crew AI.

• Excellent problem-solving skills, attention to detail, and ability to work in cross-functional teams.

Nice-to-have Requriements:

• Familiarity with real-time messaging and event-driven systems (Kafka, RabbitMQ, SNS/SQS, Event Grid).

Offer description

We are seeking a highly skilled AI/ML Engineer / Python Developer to design, develop, and deploy cutting-edge machine learning and AI solutions. You will work on large-scale, distributed systems, leveraging modern ML frameworks, cloud platforms, and vector databases to build intelligent applications. This is a hands-on role that combines data engineering, model development, and MLOps to drive production-ready AI services.

Your responsibilities

1. Design, develop, and deploy machine learning and NLP models using Python, TensorFlow, PyTorch, and Hugging Face (experience with scikit-learn, spaCy, NLTK, or Gensim is a plus).
2. Process and analyze large-scale datasets using Pandas, NumPy, and Dask, and manage structured data using SQL (PostgreSQL, MySQL) (NoSQL experience—MongoDB, Cassandra—is nice to have.)
3. Implement and manage vector search and semantic retrieval systems using FAISS(experience with Pinecone, Weaviate, or Milvus is a plus).
4. Deploy ML models and pipelines on a major cloud platform, preferably AWS(Azure or GCP experience is equally welcome).
5. Develop and maintain API services using FastAPI or Flask to expose ML models for internal and external use.
6. Implement MLOps and CI/CD pipelines using Git and GitHub Actions (Azure DevOps or Jenkins are also acceptable), including containerization with Docker and orchestration with Kubernetes.

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