AI Engineer

Job Summary:

AI Engineer Product Integration (LLMs & ML Systems)

We are seeking a hands-on AI Engineer with proven experience in building, fine-tuning, and deploying Large Language Models (LLMs) and Machine Learning (ML) systems in production. This is not a research role were looking for someone who can turn models into scalable features, deeply integrated within our Optimiser CRM platform.

Youll lead initiatives that turn AI into real value: automating workflows, enriching CRM data, and enhancing decision- making across our client base. This role requires strong engineering fundamentals, model fluency, and the ability to build reliable AI solutions in a multi-tenant environment.

Key Responsibilities:

AI/ML Development

• Design and fine-tune LLMs and ML models for specific CRM workflows.
• Build and optimize data pipelines for training, inference, and evaluation.
• Implement retrieval-augmented generation (RAG) and context injection strategies.

Production Integration

• Package and deploy models into scalable production systems.
• Develop APIs and microservices for AI-powered features.
• Monitor model behavior in production; use telemetry to drive continuous improvement.

Architecture & Compliance

• Ensure AI features adhere to multi-tenant data isolation and privacy standards.
• Collaborate with product and backend teams to align AI systems with Optimisers architecture.
• Implement access control, audit logging, and performance monitoring for AI services.

Qualifications

• 2+ years of experience shipping ML/LLM features in a production environment.
• Strong Python development skills and hands-on experience with ML/LLM libraries (e.g., PyTorch, TensorFlow, LangChain, Hugging Face).
• Practical understanding of vector search, embeddings, and retrieval systems (e.g., Pinecone, FAISS, Weaviate).
• Experience deploying models as part of SaaS applications, especially in privacy-sensitive environments.
• Clear understanding of the ML lifecycle: from development to monitoring and iteration.

Preferred (Not Mandatory)

• Exposure to prompt engineering and model evaluation techniques.
• Experience with fine-tuning LLMs or working with tools like LoRA, PEFT, or RAG pipelines.
• Familiarity with agent-based orchestration (e.g., LangChain agents, semantic routers).
• Understanding of MLOps (model versioning, deployment automation, rollback strategies).

Location:

Location: Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad