AI Engineer
Hybrid · Bengaluru, Karnataka, India
Job Summary
Advanced AI Engineer role focused on designing, building, and deploying large-scale AI and Generative AI systems that drive business impact. Collaborate with product, platform, and business teams to translate complex problems into scalable AI solutions. Responsibilities include end-to-end AI/ML/GenAI development, multi-agent and orchestration design, deployment and operationalization with MLOps, production-grade Python development, integration with enterprise systems, and governance/safety guardrails. Strong emphasis on modern ML tooling, vector databases, embeddings, semantic search, cloud deployment (Azure), containers, Kubernetes, model monitoring, and continuous improvement.
Required Qualifications
- Design, develop, and deploy end-to-end AI/ML and GenAI solutions across structured and unstructured data
- Build and fine-tune Large Language Models (LLMs) using techniques such as fine-tuning, LoRA/QLoRA, prompt engineering, RAG, agents, and tool-calling
- Implement advanced models including transformers, diffusion models, graph ML, time-series models, and reinforcement learning where applicable
- Perform model evaluation, bias/fairness checks, explainability (XAI), and continuous performance optimization
- GenAI & Agentic Systems design multi-agent workflows for complex reasoning, orchestration, and autonomous task execution
- Build systems leveraging vector databases, embeddings, semantic search, and knowledge graphs
- Implement guardrails for safety, hallucination reduction, and responsible AI
- MLOps & Platform Engineering Productionize AI models using CI/CD pipelines, model versioning, feature stores, and model serving frameworks
- Deploy scalable solutions on cloud platforms (Azure) using containers and Kubernetes
- Monitor model drift, performance, cost, and reliability in production environments
- Software & Data Engineering Write production-grade Python code with strong testing, logging, and observability
- Integrate AI services with enterprise systems via APIs, event-driven architectures, and data pipelines
- Collaborate with data engineers on feature engineering, data quality, and governance
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