Lead AI Engineer
Gurugram, Haryana, India
INR 3000K-5000K (estimate) Senior-level Full Time
Tasks
- Architect multi-agent systems
- Build guardrails and hallucination mitigation
- Build observability for LLM systems
- Define and implement LLM evaluation suites
- Deploy using Docker and Kubernetes
- Design retrieval pipelines for RAG
- Implement CI/CD pipelines for ML
- Implement model adaptation and prompt optimization
- Lead technical discovery and solutioning
- Mentor engineers and review architecture
- Optimize inference cost and tokens
- Optimize model serving throughput
- Optimize retrieval with hybrid search
- Present solution designs and demos to stakeholders
- Productionize AI services on AWS
- Productionize AI services on Azure
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agent Framework | Arize Phoenix | Autogen | Azure | Batching | CI/CD | Content Filtering | CrewAI | DeepSpeed | Docker | Embeddings | FastAPI | G-Eval | GraphRAG | Guardrails | Hallucination mitigation | Hybrid search | Inference Server | Injection Defense | KV cache | Knowledge graphs | Kubernetes | LLM Evaluation | Langgraph | Langsmith | LoRA | Microsoft Agent | Microsoft Agent Framework | Model routing | Multi-hop reasoning | Observability | PEFT | Pinecone | Prompt engineering | Prompt injection | Prompt injection defense | Python | Qdrant | Quantization | Query Expansion | RAG | Ragas | Re-ranking | Retrieval-Augmented Generation | SQL | Semantic Kernel | State machines | Streaming | Token optimization | Tool Registries | Triton Inference | Triton Inference Server | TruLens | VLLM | Vector Databases | Weaviate
Roles
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