AI/ML Engineer

Role Summary :

We are building next-generation AI-driven assistants to transform the way professionals learn, buy, and receive support. As the Lead AI/ML Engineer, you will work closely with the CTO and take ownership of the architecture, development, and deployment of LLM-powered solutionsincluding custom model training, RAG pipelines, and multi-agent systems.

This is a full-stack AI role, requiring expertise across LLM engineering, backend APIs, infrastructure, DevOps, and observability. You will play a critical role in ensuring our GenAI stack is production-ready, scalable, and automated to deliver real-world impact.

Key Responsibilities :

- Lead the design, development, and scaling of AI-first features and applications for production use.

- Architect and implement custom LLM training and fine-tuning on proprietary datasets (e.g., SFT, LoRA, PEFT).

- Build and optimize RAG pipelines with vector stores (e.g., Pinecone, FAISS) and embeddings.

- Drive the adoption of LangChain, LangGraph, and prompt engineering best practices.

- Own the end-to-end AI engineering lifecyclefrom model development to infrastructure and app integration.

- Implement cloud-native solutions (AWS ECS, Lambda, Bedrock, S3, IAM) and ensure robust DevOps practices.

- Develop and deploy APIs using FastAPI or Node.js, with CI/CD pipelines (GitHub Actions, AWS

CodePipeline).

- Set up observability and monitoring across the GenAI stack (Langfuse, LangWatch, OpenTelemetry, Grafana, CloudWatch).

- Collaborate cross-functionally with product, design, and business teams to rapidly experiment, iterate, and ship solutions.

Must-Have Skills :

- Proven expertise in LLM fine-tuning and custom model development using proprietary datasets.

- Should have 5+ years of experience in similar role.

- Strong experience with RAG architectures, embeddings, and vector databases (Pinecone, FAISS).

- Hands-on proficiency with LangChain, LangGraph, and prompt engineering.

- Solid cloud experience (preferably AWS : ECS, Lambda, Bedrock, S3, IAM).

- Strong backend/API engineering skills (FastAPI, Node.js) with Docker, Git, CI/CD (GitHub Actions, AWS CodePipeline).

- Scripting proficiency in Python and Bash.

- Experience with workflow automation tools (e.g., n8n).

- Observability tools knowledge : Langfuse, LangWatch, OpenTelemetry.