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Lead AI Engineer – Generative & Agentic AI

ThreatXIntel
Chennai, IND
Full-time
Associate
Onsite
Discovered 1 weeks ago
PythonSQLMachine learningDeep learningPyTorchTensorFlow
Free

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Company and Hiring Context

ThreatXIntel is a cybersecurity, IT staffing, and consulting company delivering technology and security solutions.

ThreatXIntel is the official hiring partner for a corporate client.

Work Arrangement

  • The role is based in Chennai and is onsite.
  • The schedule is five days working from the office.
  • The employment type is full-time.

About the Role

The posting covers Data Scientists in Generative AI at Engineer and Lead levels.

The role involves enterprise-grade Generative AI and Agentic AI applications using LLMs, RAG, AI Agents, multimodal AI, and cloud-native infrastructure.

The work includes LLM orchestration, RAG pipelines, vector databases, model serving, AI microservices, MLOps/LLMOps, and scalable cloud deployments.

Key Responsibilities

  • Design, build, and deploy production-grade Generative AI and Agentic AI applications.
  • Develop end-to-end GenAI pipelines from data preparation through deployment and monitoring.
  • Build RAG, semantic search, hybrid search, embeddings, and reranking solutions.
  • Integrate GPT, Claude, LLaMA, and Mistral into production applications.
  • Develop AI workflows with LangChain, LlamaIndex, Hugging Face, OpenAI, Anthropic, and AutoGen.
  • Build multi-agent workflows and enterprise Agentic AI solutions.
  • Work with Pinecone, FAISS, Milvus, Weaviate, and ChromaDB.
  • Optimize inference, latency, throughput, and infrastructure cost.
  • Develop AI microservices, REST APIs, GraphQL services, and Kafka-based services.
  • Deploy workloads with Docker, Kubernetes, Azure, AWS, or GCP.
  • Implement MLOps/LLMOps, CI/CD, monitoring, observability, and model lifecycle management.
  • Lead candidates own architecture, mentor, conduct code reviews, and lead enterprise initiatives.

Required Skills

  • Lead candidates should have 8–12 years of experience; engineer candidates should have 5–8 years.
  • Required technical areas include Python, SQL, machine learning, deep learning, PyTorch, and TensorFlow.
  • Required GenAI areas include Generative AI, Agentic AI, AI Agents, multi-agent systems, and prompt engineering.
  • Required frameworks include LangChain, LlamaIndex, Hugging Face Transformers, OpenAI API, Anthropic API, and AutoGen.
  • Required search technologies include RAG, embeddings, chunking, semantic search, hybrid search, and reranking.
  • Required infrastructure includes vector databases, Docker, Kubernetes, cloud platforms, APIs, microservices, CI/CD, MLOps, and LLMOps.

Preferred Qualifications

  • Production-scale GenAI platform development is preferred.
  • Enterprise Agentic AI experience is preferred.
  • Multimodal AI experience is preferred.
  • Enterprise AI architecture experience is preferred.
  • Secure and scalable cloud-native AI solution experience is preferred.

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