Sr. AI Engineer

Saison International; Bengaluru; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Saison International in Bengaluru, India is seeking a Senior/Lead AI Engineer to design and scale production-grade AI systems across enterprise contexts. This hands-on role sits at the intersection of LLMs, multimodal AI, retrieval, and ML engineering, steering projects from experimentation to production deployment.

You will work with product, engineering, data science, and business teams to translate AI capabilities into measurable business outcomes, mentor engineers, and own the technical

Qualifications

• 5+ years of AI/ML engineering experience with production deployments
• Strong hands-on experience building LLM/generative AI applications
• Proficient in Python and SQL
• Experience with PyTorch/TensorFlow and modern ML frameworks
• Familiarity with Hugging Face Transformers and LLM ecosystems
• Understanding of LLM fine-tuning (LoRA/PEFT) and retrieval systems
• Experience with vector databases (FAISS, Pinecone, Weaviate, Milvus)
• Experience deploying AI workloads on AWS/GCP/Azure
• Knowledge of Docker, Kubernetes, CI/CD and ML deployment practices
• Experience with model evaluation, observability and monitoring tools

Responsibilities

• Design and build enterprise-scale Generative AI solutions across LLMs, multimodal systems, and agentic AI
• Own the technical roadmap for AI problem areas from experimentation to production deployment
• Build production applications using leading foundation models (GPT, Claude, Llama, Mistral)
• Design robust RAG and enterprise retrieval platforms with embedding strategies and vector databases
• Fine-tune LLMs using LoRA/PEFT for efficient specialization
• Develop agentic AI workflows with reasoning, tool usage, API integration and orchestration
• Build multimodal AI systems integrating text, images, documents, audio, and other data
• Define frameworks for evaluation, quality benchmarking, observability, and production monitoring
• Optimize AI systems for latency, throughput, accuracy, and cost
• Establish engineering standards for experimentation, security, governance, and production readiness
• Mentor engineers and provide technical leadership across AI initiatives
• Translate AI capabilities into measurable business outcomes for stakeholders

Job description

We are looking for a Senior/Lead AI Engineer to build and scale production-grade AI systems across Credit Saison India. This is a highly hands-on role for someone who can work at the intersection of LLMs, generative AI, retrieval systems, multimodal AI, agentic workflows, and ML engineering, taking problems from experimentation and architecture through production deployment and optimization. You will work closely with product, engineering, data science, and business teams to build AI capabilities that can operate reliably at enterprise scale.

Responsibilities:

• Design and build enterprise-scale Generative AI solutions across LLMs, multimodal systems, and agentic AI.
• Own the technical roadmap for key AI problem areas from experimentation and prototyping to production deployment.
• Build production applications using leading foundation models such as GPT, Claude, Llama, Mistral, and other open-source/commercial models.
• Design robust RAG and enterprise retrieval platforms, including document ingestion and chunking, embedding strategies, semantic and hybrid retrieval, reranking, vector databases, retrieval evaluation, and optimization.
• Build and fine-tune LLMs using techniques such as LoRA, PEFT, and parameter-efficient fine-tuning.
• Develop agentic AI workflows capable of reasoning, tool usage, API integration, orchestration, and multi-step execution.
• Build multimodal AI systems combining text, image, document, audio, or other enterprise data sources.
• Define frameworks for model evaluation, quality benchmarking, hallucination detection, observability, and production monitoring.
• Optimize AI systems for latency, throughput, accuracy, reliability, and inference cost.
• Design scalable ML/LLM deployment pipelines using cloud-native and containerized environments.
• Establish engineering standards around experimentation, evaluation, security, governance, monitoring, and production readiness.
• Mentor engineers and provide technical leadership across AI initiatives.
• Translate complex AI capabilities into measurable business outcomes for technical and non-technical stakeholders.

Requirements:

• 5+ years of strong AI/ML engineering experience, with meaningful experience deploying AI/ML systems into production.
• Strong hands-on experience building LLM / generative AI applications.
• Strong programming expertise in Python and SQL.
• Hands-on experience with PyTorch / TensorFlow and modern ML/AI frameworks.
• Experience with Hugging Face Transformers and the modern LLM ecosystem.
• Strong understanding of LLM fine-tuning, including LoRA / PEFT.
• Production experience building RAG/enterprise retrieval systems.
• Strong understanding of embeddings, semantic search, vector retrieval, reranking, prompt engineering, and context optimization.
• Experience with vector databases such as FAISS, Pinecone, Weaviate, Milvus, or equivalent.
• Experience with orchestration frameworks such as LangChain, LlamaIndex, or similar frameworks.
• Experience deploying AI workloads on AWS, GCP, or Azure.
• Strong understanding of Docker, Kubernetes, CI/CD, and ML deployment practices.
• Experience with model evaluation, observability, and monitoring using tools such as MLflow, Weights and Biases, LangSmith, or equivalent.
• Strong problem-solving ability and experience taking AI systems from POC production scale.

Strong Plus:

• Experience building agentic AI systems, tool-calling workflows, or multi-agent architectures.
• Experience with multimodal models.
• Experience designing enterprise-scale retrieval platforms.
• Understanding of model serving, inference optimization, and GPU workloads.
• Experience defining AI governance, auditability, security, and responsible-AI controls.
• Strong foundation across traditional machine learning, NLP, and deep learning.
• Experience mentoring engineers or leading technically complex AI initiatives.