Senior AI/ML Engineer (LLM, Python)

Key Skills Python Java AWS Natural Language Processing (NLP) DynamoDB TensorFlow PyTorch Chatbot Development Agile Development RAG (Retrieval-Augmented Generation) Machine Learning Vector Databases Embedding Models Large Language Models (LLM) EC2 Lambda SageMaker CloudWatch API Gateway Hugging Face Transformers LangChain Generative AI Cloud Infrastructure Team Leadership Technical Project Management SQL Deep Learning Azure Github Actions Docker Kubernetes Prometheus Grafana Apache Kafka Apache Airflow Scikit-Learn Pandas Numpy Bedrock Azure AI Foundry LangGraph CrewAI MLflow Weights & Biases Azure DevOps Transformers Embeddings Document Parsing Text Processing REST API Design Agentic Workflows Roles & 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 robust, scalable AI/ML services in Python with focus on reliability and production-grade performance Develop and optimize AI-driven extraction workflows using document parsing, chunking, embeddings, RAG, and LLM-based extraction methods Deploy and scale AI models on AWS and Azure (SageMaker, Bedrock, Azure AI Foundry) with seamless integration into data pipelines Build and maintain CI/CD pipelines for AI model deployment using GitHub Actions, Azure DevOps, Docker, and Kubernetes Define and implement 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 translate business requirements into scalable AI solutions Mentor team members and share knowledge to elevate overall team capability Continuously research and apply advancements in NLP, LLMs, and extraction techniques Contribute to efficient development cycles following Agile practices and drive automation across the AI delivery pipeline Ideal Candidate 1 Strong Senior AI Engineer Profile with production-grade LLM and Python expertise 2 Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments 3 Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design 4 Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering) 5 Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows. 6 Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale 7 Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI) 8 Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes) 9 Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs). 10 Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges 11 Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation 12 Mandatory (Company): B2B Tech/Consulting/B2B Software 13 Preferred (Certification): AWS certifications (Solutions Architect, Machine Learning Specialty), Azure certifications (AI Fundamentals, AI Engineer Associate), or Databricks certifications (Machine Learning Professional, Generative AI Engineer Associate) 14 Preferred (Methodology): Experience in Agile development methodologies 15 Preferred (Tools): Data pipeline/orchestration (Apache Kafka, Airflow); certifications — AWS (Solutions Architect, ML Specialty), Azure (AI Engineer Associate), Databricks (ML Professional, GenAI Engineer Associate). Additional Information Full-time position based in Hyderabad, Indore, or Ahmedabad, India Role involves end-to-end development lifecycle including solution architecture, design, implementation, deployment, and scaling of AI applications Work with a passionate team in a fast-paced, growth-oriented environment on high-impact, real-world AI projects across diverse industries with global clients Company is CMMI Level 3 Appraised, rated 4.6/5 on Glassdoor, and recognized for excellence in data and AI solutions with multiple industry awards Kanerika locations include Austin (USA), Singapore, Hyderabad, Indore, and Ahmedabad (India) Interview Process Tech round Tech round Tech+managerial round Final CTC discussion