Senior AI/ML Engineer (LLM, Python)

About the role

Kanerika is hiring a Senior AI/ML Engineer focused on LLMs and Python, working on end-to-end production-grade AI solutions such as RAG pipelines, LLM-based applications and extraction systems. The position is full-time and based in Hyderabad, Indore or Ahmedabad, India, within a fast-paced, growth-oriented team serving global clients.

Key responsibilities

• Lead end-to-end design and delivery of production-grade AI/ML solutions including RAG pipelines, LLM-based applications and extraction systems.
• Architect and develop scalable AI/ML services in Python, focusing on reliability and performance.
• Build and optimize AI-driven extraction workflows using document parsing, chunking, embeddings, RAG and LLM-based extraction.
• Deploy and scale AI models on AWS and Azure (SageMaker, Bedrock, Azure AI Foundry) and integrate them into data pipelines.
• Build and maintain CI/CD pipelines for model deployment using GitHub Actions, Azure DevOps, Docker and Kubernetes.
• Define evaluation frameworks (precision, recall, F1, field-level accuracy) and maintain code quality through reviews and testing.
• Partner with Product, Data Engineering and Platform teams to turn business requirements into scalable AI solutions.
• Mentor team members, mentor knowledge sharing, follow Agile practices and drive automation across the AI delivery pipeline.

Required profile

• At least 3 years of professional AI/ML engineering experience delivering production-grade AI systems.
• B.Tech or B.E.; graduates with BCA or BCS must hold a Master's or post-graduation degree.
• Experience leading projects or teams, managing technical deliverables and mentoring.
• Background in B2B tech, consulting or B2B software.
• Preferred: AWS, Azure or Databricks certifications; Agile experience; data pipeline orchestration (Kafka, Airflow).

Required skills

• Python and SQL, with scikit-learn, pandas, numpy and PyTorch or TensorFlow.
• RAG pipelines, document parsing, information extraction, transformers, embeddings, vector databases and agentic workflows (LangChain, LangGraph, CrewAI, Claude Code).
• AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for training, fine-tuning, deployment and inference.
• MLOps: MLflow, Weights & Biases, model versioning, CI/CD, Prometheus, Grafana, SLOs.