ThreatXIntel
Website:
threatxintel.com
Company:
https://www.linkedin.com/company/threatxintel
Seniority: Associate
Industries: Software Development, IT Services and IT Consulting, and IT System Custom Software Development
Job details:
Company Description
ThreatXIntel is a growing Cybersecurity, IT Staffing, and Consulting company delivering end-to-end technology and security solutions.
We are hiring for our corporate client. ThreatXIntel is the official hiring partner for this requirement.
📍 Location: Chennai (Onsite – 5 Days/Week)
💼 Employment Type: Full-Time
🎯 Experience:
- Engineer: 5–8 Years
- Lead: 8–12 Years
⏳ Notice Period: Immediate to 15 Days Preferred
About the Role
We are looking for experienced and passionate Data Scientists – Generative AI (Engineer & Lead) to join our AI Engineering team in Chennai.
The ideal candidate will have strong hands-on experience in designing, developing, and deploying enterprise-grade Generative AI and Agentic AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, Multimodal AI, and cloud-native AI infrastructure.
You will work on next-generation AI solutions involving LLM orchestration, RAG pipelines, vector databases, model serving, AI microservices, MLOps/LLMOps, and scalable cloud deployments.
Key Responsibilities
- Design, build, and deploy enterprise-grade Generative AI and Agentic AI applications.
- Build end-to-end GenAI pipelines from data collection, preprocessing, model development, evaluation, deployment, and monitoring.
- Develop scalable Retrieval-Augmented Generation (RAG) pipelines for enterprise use cases.
- Integrate Large Language Models (GPT, Claude, LLaMA, Mistral) into production systems.
- Develop AI workflows using LangChain, LlamaIndex, Hugging Face, OpenAI APIs, Anthropic APIs, and AutoGen.
- Design and implement multi-agent AI workflows and enterprise Agentic AI solutions.
- Build embedding pipelines, semantic search, hybrid search, reranking, and document intelligence solutions.
- Implement vector database solutions using Pinecone, FAISS, Milvus, Weaviate, and ChromaDB.
- Optimize model inference, latency, throughput, and infrastructure cost using vLLM, Ollama, quantization, PEFT, LoRA, and QLoRA.
- Develop scalable REST APIs, GraphQL services, Kafka-based services, and AI microservices.
- Deploy AI workloads using Docker, Kubernetes, and Azure, AWS, or GCP.
- Implement enterprise MLOps and LLMOps practices, including CI/CD, testing, monitoring, observability, and model lifecycle management.
- Develop multimodal AI solutions involving text, vision, image, speech, audio, and video generation using models such as Stable Diffusion and DALL·E.
- Collaborate with Product, Engineering, Data Science, and Business teams to deliver enterprise AI solutions.
- For Lead roles: Drive technical architecture, provide technical leadership, mentor AI engineers, perform code reviews, define AI best practices, and lead enterprise-scale AI initiatives.
Required Skills
Programming & AI
- Python
- SQL
- Machine Learning
- Deep Learning
- PyTorch
- TensorFlow
Large Language Models
Generative AI
- Generative AI
- Agentic AI
- AI Agents
- Multi-Agent Systems
- Prompt Engineering
AI Frameworks
- LangChain
- LlamaIndex
- Hugging Face Transformers
- OpenAI API
- Anthropic API
- AutoGen
RAG & Search
- Retrieval-Augmented Generation (RAG)
- Embeddings
- Chunking
- Semantic Search
- Hybrid Search
- Reranking
Vector Databases
- Pinecone
- FAISS
- Weaviate
- Milvus
- ChromaDB
Model Deployment & Optimization
- vLLM
- Ollama
- Model Quantization
- PEFT
- LoRA
- QLoRA
Multimodal AI
- Stable Diffusion
- DALL·E
- Image Generation
- Vision AI
- Speech AI
- Audio AI
- Video Generation
Cloud & Azure
- Azure AI Foundry
- Azure AI Search
- Azure OpenAI
- AWS
- Azure
- GCP
DevOps & Infrastructure
- Docker
- Kubernetes
- REST APIs
- GraphQL
- Kafka
- Microservices
- CI/CD
- MLOps
- LLMOps
- AI Observability
- Model Monitoring
Preferred Qualifications
- Experience building production-scale Generative AI platforms.(PYTHON)
- Experience with enterprise Agentic AI systems.
- Hands-on experience with multimodal AI applications.
- Strong understanding of enterprise AI architecture.
- Experience with secure and scalable cloud-native AI solutions.
Click on Apply to know more.