The ideal candidate will have strong expertise in machine learning, deep learning, Large Language Models (LLMs), cloud platforms, MLOps, and AI solution architecture.

This role requires both strategic architectural thinking and hands-on engineering capabilities to build AI products that drive business value.

Key Responsibilities :

- Define and implement enterprise AI architecture aligned with business objectives.

- Design scalable, secure, and production-ready AI/ML systems and platforms.

- Lead architecture discussions for AI, Machine Learning, Generative AI, and data-driven applications.

- Evaluate emerging AI technologies, frameworks, and tools to drive innovation.

- Establish AI governance, model lifecycle management, and responsible AI practices.

- Design, develop, train, and deploy machine learning and deep learning models.

- Build predictive analytics, recommendation systems, NLP, computer vision, and GenAI solutions.

- Develop and optimize Large Language Model (LLM) applications using prompt engineering, RAG, fine-tuning, and agentic workflows.

- Evaluate model performance and continuously improve accuracy, scalability, and efficiency.

- Architect and implement GenAI solutions using OpenAI, Anthropic Claude, Gemini, Llama, Mistral, or similar models.

- Build Retrieval-Augmented Generation (RAG) pipelines using vector databases.

- Design conversational AI systems, AI assistants, chatbots, and autonomous agents.

- Develop prompt engineering frameworks and AI evaluation mechanisms.

- Implement model monitoring, guardrails, safety controls, and AI observability.

- Collaborate with data engineering teams to design scalable data pipelines.

- Integrate AI solutions with enterprise applications, APIs, databases, and cloud services.

- Design feature stores, knowledge bases, embeddings pipelines, and vector search architectures.

- Ensure high-quality data processing, governance, and compliance standards.

- Design AI infrastructure on AWS, Azure, or GCP.

- Build CI/CD pipelines for AI model deployment and lifecycle management.

- Implement MLOps practices including model versioning, monitoring, retraining, and automation.

- Optimize AI workloads for performance, scalability, reliability, and cost efficiency.

- Provide technical leadership and mentorship to AI/ML engineers and data scientists.

- Work closely with business stakeholders to translate business requirements into AI solutions.

- Conduct architecture reviews, technical design sessions, and proof-of-concept initiatives.

- Drive best practices in AI engineering, software development, and system architecture.

Required Skills :

AI & Machine Learning :

- Machine Learning Algorithms

- Deep Learning

- Natural Language Processing (NLP)

- Computer Vision

- Reinforcement Learning (preferred)

- Predictive Analytics

- Recommendation Systems

Generative AI :

- Large Language Models (LLMs)

- Prompt Engineering

- RAG (Retrieval-Augmented Generation)

- AI Agents & Agentic Frameworks

- Fine-Tuning & Model Optimization

- Embeddings & Vector Search

- LLM Evaluation Frameworks

Programming:

- Python (Mandatory)

- SQL

- Java/Scala (Preferred)

- REST APIs

- Microservices Architecture

Frameworks & Libraries :

- TensorFlow

- PyTorch

- Scikit-learn

- LangChain

- LlamaIndex

- Hugging Face

- FastAPI

- MLflow

MLOps & DevOps :

- Docker

- Kubernetes

- CI/CD Pipelines

- Model Monitoring

- Model Registry

- Experiment Tracking

- Airflow

Databases :

- PostgreSQL

- MySQL

- MongoDB

- Redis

- Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS)

Cloud Platforms :

- AWS (SageMaker, Bedrock, Lambda, ECS/EKS, S3)

- Azure AI Services

- Google Vertex AI

Data Technologies :

- Spark / PySpark

- Data Warehousing

- ETL/ELT Pipelines

- Data Lakes

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.

- 8 - 13 years of overall software engineering and AI/ML experience.

- Proven experience architecting and deploying enterprise AI solutions in production.

- Experience leading AI transformation initiatives and cross-functional teams.

- Strong understanding of software architecture, distributed systems, and cloud-native technologies.

- Experience building enterprise GenAI applications.

- Hands-on experience with OpenAI, Claude, Gemini, or open-source LLMs.

- Exposure to Agentic AI, Multi-Agent Systems, and AI Orchestration frameworks.

- Experience in regulated industries such as BFSI, Healthcare, FinTech, or SaaS.

- Knowledge of Responsible AI, AI Governance, Security, and Compliance frameworks.

- Successful deployment of scalable AI solutions into production.

- Improved model performance, reliability, and business outcomes.

- Reduced deployment cycle through MLOps automation.

- Increased adoption of AI capabilities across business functions.

- Strong architecture governance and AI platform standardization.