Data Scientist

We are seeking experienced Data Scientists/AI Engineers to design, build, and deploy production-grade AI and generative AI solutions. The role requires expertise in machine learning, deep learning, NLP, and GenAI engineering (RAG, agent-based systems), along with hands-on cloud deployment and MLOps experience.

Responsibilities:

• Develop ML/DL models for real-world business problems.
• Build NLP solutions (semantic search, entity recognition, text generation).
• Design and implement RAG systems and agent-based AI workflows.
• Build LLM-powered enterprise applications.
• Architect end-to-end AI/GenAI solutions.
• Implement MLOps pipelines (CI/CD, monitoring, governance).
• Deploy solutions on GCP and have strong experience with Vertex AI.
• Use Docker and Kubernetes for scalable deployments.
• Collaborate with stakeholders and mentor junior team members.

Requirements:

• 8-12 Years: Architecture ownership, leadership, and solution architecture. Strong knowledge of GCP Vertex AI and GenAI concepts for building end-to-end applications.
• 4-7 Years: Strong hands-on development and deployment experience. Strong knowledge of GCP Vertex AI and GenAI concepts for building end-to-end applications.
• Candidates with production-grade AI/GenAI experience.
• Strong engineering mindset with deployment focus.
• Ability to build scalable, enterprise-ready AI solutions.

Mandatory Skills:

• Machine Learning and Deep Learning (hands-on).
• Strong Python development.
• NLP: semantic search, entity recognition, text generation.
• Generative AI: RAG, agentic workflows, LangChain/LangGraph.
• AI architecture and production deployment.
• MLOps / AIOps practices.
• Cloud platforms: GCP.
• Containerization: Docker, Kubernetes.
• AI governance and stakeholder management.

Good to Have:

• Vector Databases (Pinecone, FAISS, Azure AI Search).
• AI observability and evaluation frameworks.
• Cost optimization for GenAI systems and token optimization techniques.