Lead AI or ML Engineer
INR 2500K-5000K (estimate) Senior-level Full Time
Tasks
- Apply MLOps best practices
- Build Agentic AI workflows
- Build generative AI solutions
- Create AI guardrails
- Create evaluation frameworks
- Define AI architecture
- Develop AI Proof-of-Concepts
- Develop reusable AI frameworks
- Drive model deployment to production
- Engineer prompts
- Ensure scalability
- Establish model governance
- Implement RAG solutions
- Implement deep learning models
- Implement machine learning models
- Implement multi-agent systems
- Implement natural language processing solutions
- Implement semantic search
- Lead AI solution design
- Lead technical team
- Mentor data scientists
- Own AI strategy
- Select AI technologies
- Set up monitoring and observability
Perks/Benefits
- Career development opportunities
- Comprehensive benefits
- Flexible work arrangements
- Inclusion culture
- Mentorship
Skills/Tech-stack
Agent systems | Agentic AI | Amazon SageMaker | Anomaly Detection | Autogen | Azure Machine Learning | CI/CD | CrewAI | Deep learning | Distributed Training | Docker | Embeddings | Explainability | Fairness | Feature Stores | GPU Optimization | Generative AI | Google Vertex | Google Vertex AI | Kubernetes | LLM orchestration | Langchain | Langgraph | Language Models | Language Processing | Large Language Models | Llamaindex | MLOps | MLflow | Machine Learning | Model Governance | Model Monitoring | Model Registry | Multi-Agent | Multi-Agent Systems | Natural Language | Natural Language Processing | Observability | Predictive Analytics | Prompt engineering | PyTorch | Python | Recommendation Systems | Responsible AI | Retrieval-Augmented Generation | SQL | Semantic Kernel | Semantic Search | Series forecasting | Statistical modeling | TensorFlow | Time Series | Time Series Forecasting | Vector Databases | Vector Retrieval | Vertex AI
Education
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