- Experience: 7+ years overall, with 2+ years of hands-on experience in AI / LLM / Generative AI

- Looking for Developers only (NO Data Scientist)

Role Overview :

We are looking for an AI/ML Architect with a strong development background and deep hands-on expertise in Generative AI, Large Language Models (LLMs), agentic AI systems, traditional ML workflows and enterprise-scale AI architectures.

This role focuses on building production-ready AI solutions, including RAG pipelines, LLM-powered chatbots, agentic AI workflows, and enterprise ML systems, while defining AI architecture, GenAI strategy, and enterprise AI transformation initiatives.

Key Responsibilities :

Generative AI & LLM Architecture

- Architect and deliver Generative AI solutions using LLMs, RAG architectures, and agentic AI frameworks

- Build end-to-end GenAI pipelines for:

- Retrieval-Augmented Generation (RAG)

- Conversational AI and LLM-based search

- Autonomous and multi-agent AI systems

- Design and optimize LLM orchestration workflows using LangChain, LangGraph, and similar

frameworks

- Lead prompt engineering, prompt optimization, evaluation, benchmarking, and LLM fine-tuning

- Work with OpenAI (GPT-4o), Claude, LLaMA, Hugging Face, and open-source LLMs

Retrieval Systems & Vector Databases

- Design and scale enterprise retrieval systems, including:

- Embedding generation

- Intelligent chunking strategies

- Vector indexing and semantic search

- Reranking for accuracy and relevance

- Hands-on experience with vector databases and search platforms such as:

- Pinecone

- PGVector

- Azure AI Search

- Amazon Titan

Enterprise ML & Data Engineering

- Develop, train, and deploy machine learning models for predictive analytics, classification, and clustering

- Implement ML solutions using TensorFlow, PyTorch, Scikit-learn

- Work with big data technologies like Apache Spark and Snowflake

- Design and optimize ETL pipelines for data transformation, quality, and validation

- Work with SQL, PostgreSQL, MySQL, MongoDB for data management and query optimization

Cloud & AI Platform Architecture

- Deploy and scale AI/GenAI solutions on:

- AWS (SageMaker, Bedrock, Lambda, S3)

- Azure (Azure ML, Azure OpenAI)

- Google Cloud (Vertex AI)

- Design secure, scalable, and compliant AI architectures aligned with enterprise IT, governance, and responsible AI practices

- Integrate AI services into enterprise applications using APIs and microservices

MLOps / GenAIOps :

- Implement LLMOps / GenAIOps / MLOps pipelines including:

- CI/CD for ML and LLM workflows

- Monitoring, evaluation frameworks, and guardrails

- Model lifecycle management and continuous improvement

Collaboration & Leadership :

- Collaborate with data scientists, ML engineers, architects, product managers, and business stakeholders

- Communicate AI architecture and design decisions clearly to technical and non-technical audiences

- Influence AI strategy, architecture, and enterprise adoption initiatives

Required Skills & Experience :

- 8+ years of experience in AI/ML engineering or architecture

- Strong programming skills in Python and SQL

- Hands-on experience with Generative AI, LLMs, RAG, and agentic AI systems

- Expertise in LangChain, LangGraph, Hugging Face, and LLM orchestration

- Experience with vector databases and semantic search

- Strong cloud experience across AWS, Azure, and/or GCP

- Solid understanding of enterprise AI architecture, security, and governance

- Experience working in production, enterprise-scale AI systems

- Strong analytical, problem-solving, and communication skills

Education :

- Bachelors degree in Computer Science, Data Science, AI/ML, or related field