Description :

As a Senior AI Engineer, you'll lead the design, development, and deployment of production-grade GenAI systems that power our next wave of intelligent products. You'll work at the intersection of AI research and scalable engineering, building LLM and multi-agent systems that deliver measurable value for B2B businesses. You'll collaborate with product and design teams to bring ideas to life, own AI architecture decisions, and mentor junior engineers to build high-quality, modular systems.

The core responsibilities for the job include the following :

Build Scalable AI Systems :

- Design and deploy GenAI pipelines (LLMs, CV, RAG, multi-agent systems).

- Architect modular, reusable AI components for multi-tenant SaaS environments.

Integrate and Scale :

- Build APIs integrating structured data, LLMs, and computer vision workflows.

- Ensure high performance, observability, and cost-efficient scaling.

Experiment and Innovate :

- Lead internal POCs on new GenAI techniques, RAG, fine-tuning, diffusion, and multimodal AI.

- Stay hands-on with frameworks like LangChain, CrewAI, and LlamaIndex.

Infrastructure and DevOps :

- Define CI/CD, containerization (Docker, Kubernetes), and IaC standards (Terraform/Helm).

- Implement monitoring and logging using Prometheus, Grafana, or Cloud Metrics.

Mentor and Collaborate :

- Guide junior engineers, review code, and enforce best practices.

- Translate complex business problems into technical AI solutions.

You'll Drive Projects Like :

- AI Sales Copilot - Enterprise conversational AI for pipeline intelligence.

- Smart Catalog Engine - Automated visual enrichment for global B2B catalogs.

- AI-powered CRM Insights - Predictive models for engagement and lead scoring.

- GenAI Automation Layer - Connecting ERPs, ecommerce, and CRM via AI workflows.

Requirements :

- 4+ years of experience in AI/ML engineering; 2+ years in GenAI productization.

- Expertise in Python, FastAPI, and microservices architecture.

- Hands-on experience with LangChain, LlamaIndex, CrewAI, or similar frameworks.

- Deep understanding of NLP, CV, embeddings, and multi-modal architectures.

- Familiarity with OpenAI, Vertex AI, or Hugging Face APIs.

- Strong foundation in cloud infrastructure (AWS/GCP/Azure) and Kubernetes.

Nice-to-have :

- Experience in B2B SaaS, CRM, or SalesTech.

- Exposure to RAG, Pinecone, FAISS, or vector DB setups.

- Some frontend exposure (React/TypeScript) for prototyping AI features.