AI ML Architect

InfoVision Inc.; Bengaluru; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

InfoVision Inc. is seeking a highly experienced ML/AI Architect to design, build, and scale enterprise-grade AI/ML and GenAI solutions. This role involves strong technical leadership across machine learning architecture and cloud-native data engineering.

The ideal candidate will innovate, establish best practices, and lead teams to deliver production-ready AI solutions. Responsibilities include architecting end-to-end ML systems, designing scalable ML pipelines, and deploying models using modern DevOps tools such as Docker and Kubernetes.

Qualifications

• Proven experience in designing and building ML systems.
• Expertise in deploying models at scale using modern tools.
• Strong leadership and mentoring experience in ML/AI teams.

Responsibilities

• Design and implement end-to-end ML systems and pipelines.
• Collaborate with teams to deliver enterprise-grade solutions.
• Continuously evaluate and improve model performance.

Skills

ML system architecture

MLOps automation

cloud-native data engineering

prompt engineering

statistical modeling

Tools

PyTorch

TensorFlow

Docker

Kubernetes

Terraform

Job description

Job Description

We are seeking a highly experienced ML/AI Architect to design, build, and scale enterprise‑grade AI/ML and GenAI solutions within our Digital Transformation Practice. The role requires strong technical leadership across ML system architecture, LLM‑based solution design, MLOps automation, and cloud‑native data engineering. The ideal candidate will drive innovation, establish best practices, and deliver production‑ready AI solutions at scale

• Architect end-to-end ML systems covering data ingestion, feature engineering, model training, deployment, and monitoring.
• Design scalable ML pipelines for classical ML, deep learning, and GenAI workloads including LLMs, RAG, embeddings, and semantic search.
• Select optimal algorithms, frameworks, and cloud platforms to address complex business and technical challenges.
• Implement model optimization strategies such as quantization, pruning, distillation, and LoRA/QLoRA fine‑tuning.
• Build LLM‑powered solutions using prompt engineering, knowledge grounding, agents, and retrieval‑augmented generation.
• Work with top LLMs (GPT-4/5, Llama 3/3.5, Qwen, Claude, Gemini) and integrate vector DBs like Pinecone, Weaviate, Chroma, Milvus.
• Evaluate and improve LLM and ML model performance, reduce hallucinations, and ensure security and compliance.
• Lead development of ML, NLP, NER, and deep learning models using Transformers, CNNs, XGBoost, and other advanced architectures.
• Drive data science workflows including EDA, feature engineering, statistical modeling, and explainability (SHAP/LIME).
• Architect MLOps pipelines (CI/CD/CT), enabling model versioning, monitoring, drift detection, and automated retraining.
• Deploy models at scale using Kubernetes, Docker, Terraform, serverless components, and GPU‑based environments with strong observability.
• Collaborate with cross‑functional teams, lead technical discussions and POCs, and mentor ML/AI teams to deliver enterprise‑grade solutions.

• LLMs / GenAI: GPT-4/5, Llama 3/3.5, Qwen, Claude, RAG, prompt engineering, LoRA/QLoRA, embeddings
• ML / DL: PyTorch, TensorFlow, Transformers, CNNs, XGBoost, NLP/NLU, NER