Vacancy description
Laksh Consultants
India, Ahmedabad
Senior AI/ML Engineer - LLM/Python in Ahmedabad, India is listed on Jobeax. Browse 30,000+ vacancies available.
Position : Senior AI/ML Engineer (LLM, Python)Overall Experience : 3 - 7 yearsLocation : Hyderabad/Indore/AhmedabadWorking Days : 5 Days from OfficeNotice Period : Immediate Joiners/Serving Notice PeriodRoles & 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.- Mentor team members and share knowledge to elevate overall team https://jobeax.com/link/slwOdVjMywkVFhD7 Requirements : - 3+ years of professional AI/ML engineering experience with a track record of delivering production-grade AI systems.- Strong programming skills in Python and SQL, with hands-on experience in ML libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow).- Hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, and text processing on large-scale unstructured data.- Strong NLP / extraction-focused ML depth : transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.- Hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, and deployment.- Experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI).- Hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes).- Experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and AI observability (Prometheus, Grafana, SLOs). (ref:hirist.tech)
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